Social Credit and the Quantification of Everyday Life

Sesame Credit’s Mediation of Power Relations in China’s Credit Culture

Mick Vierbergen

1 University of Amsterdam RMA Thesis Cultural Analysis 24 February 2019

Student Mick Vierbergen Student number: 10662235 [email protected]

Supervisor Prof. dr. ir. Jeroen de Kloet [email protected]

Second reader Dr. Daan Wesselman [email protected]

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Table of Contents

Introduction ...... 4 Chapter 1: Alibaba and the Chinese government ...... 13 1.1 Introduction ...... 13 1.2 China’s 2020 National Social Credit System ...... 13 1.3 Alibaba’s business ecosystem ...... 21 1.4 China’s credit culture ...... 26 Chapter 2: Sesame Credit as a Technological Platform ...... 29 2.1 Introduction ...... 29 2.2 A technical walkthrough of Sesame Credit ...... 32 2.3 Disassembling Sesame Credit: technology and content ...... 37 2.4 Reassembling social credit ...... 48 Chapter 3: Explicit Users of Sesame Credit ...... 52 3.1 Introduction ...... 52 3.2 Self-reflection ...... 55 3.3 Everyday user practices ...... 58 3.4 Motivations for usage ...... 65 List of Abbreviations ...... Error! Bookmark not defined.

3 Introduction

In early 2015, China’s central bank, the People’s Bank of China, requested eight leading tech companies in the Chinese market to develop credit scoring systems, including Alipay’s parent company Ant Financial and WeChat’s Tencent. This incentive was intended to restore trust in market transactions between consumers and businesses, and, on a grander scale, in China’s economy as a whole. As the economy has until recently primarily been cash-based, the credit scoring initiatives were solutions to the shortage of personal credit records in China (Creemers 23). Credit scores would presumably allow lower waged citizens, who lack a credit history, to apply for small loans (S. Ahmed). This way, the government attempted to ‘catch up’ with credit economies like that of the United States. The commercial credit systems were initially licenced as ‘pilots’, next to local government-run pilots, for a nation-wide credit system that is to be constructed by 2020. This system encompasses a broader meaning of ‘credit’; in short, the national Social Credit System (社会信用系统 / shèhuì xìnyòng xìtǒng; hereafter ‘SCS’) not only aims to monitor and manage financial credibility of citizens and companies, but also moral credibility (Ohlberg, Ahmed and Lang 6). The Social Credit System evaluates individuals and institutions – commercial as well as governmental – on their economic and social behaviour based on massive data collection (Meissner 2). These ratings are the basis of a reward/punishment system that fosters ‘good’ behaviour and penalises ‘bad’ behaviour. As such, the SCS is a tool that allows the Chinese government to excite fine-tuned economic and social control. In June 2017, however, the People’s Bank of China (PBoC) did not extend the official licences to any of the commercial credit systems to serve as a pilot under the government project of the SCS. This was partly due to differences in interest (Ohlberg, Ahmed and Lang 12) and an apparent lack of quality of the credit systems in the eyes of the central bank. Nevertheless, there are still close ties between the government and the eight companies. After refusing to extend the licences, the PBoC established a united credit scoring bureau with the eight companies, called Baihang Credit (百行征信 / bǎixíng zhēng xìn). With this, the Chinese government can keep a close watch on the commercial scoring systems and remains relatively in control over the initiatives (Creemers 25).

4 The most notable and successful of these commercial pilots – and the object of analysis in this thesis – is Sesame Credit (芝麻信用 / zhīma xìnyòng; also called Zhima Credit), developed by Alibaba’s subsidiary Ant Financial. Sesame Credit is a consumer credit scoring service that indicates the financial credibility of users, much like FICO scores in the US. It is an opt-in service within the mobile payment application Alipay Wallet. As traditional credit information is scarce, Sesame Credit uses data from the payment platform and Alibaba affiliated businesses – such as the e-commerce websites and Alipay-connected restaurants, third-party companies, and even certain government bureaus – to compute a personal score between 350 and 950 (S. Ahmed). Although the algorithmic calculation is blackboxed, the application identifies five categories of data that are used to compute the score: credit history (e.g. from borrowed loans); contract fulfilment capacity (e.g. electricity and gas bills); personal characteristics (age, education, occupation, place of residence, etc.); behaviour and preference (e.g. usage of the Alipay app and shopping behaviour on Alibaba- owned commercial websites such as Taobao); and interpersonal relations (based on contacts in the Alipay app). Users with high Sesame scores are rewarded with privileges such as cheap loans, deposit waivers on bike or car rentals, and priority lanes at airports, and even expedited visa applications for certain countries (Ohlberg, Ahmed and Lang 12). After the People’s Bank of China did not grant Sesame Credit a licence, it functions – at least for the time being – as an autonomous commercial credit system, relatively disconnected from the Social Credit System. Several scholars have pointed out, however, that the relation between commercial actors such as Ant Financial and the government is still ambiguous, and the development of this relationship requires close attention as the construction of the SCS unfolds (Ohlberg, Ahmed and Lang 13; Creemers 27). Even at this moment, while the two credit systems are allegedly separate from each other, close ties between Sesame Credit and the government remain. Local governments, for example, use Sesame scores to waiver deposits on public services such as healthcare and social housing (Creemers 24), and conversely, Sesame Credit denies people blacklisted by the Supreme People’s Court from purchasing luxury goods at Alibaba owned e-commerce websites Taobao and Tmall (S. Ahmed). Furthermore, as state interventions on companies and internet platforms are not uncommon in China, it remains unclear what the role of Sesame Credit will be in relation to the SCS or other government regulations and how its user base will be affected.

5 Journalistic and academic context While these developments have sparked nightmarish imaginations in English language media, Chinese media seem to be less openly critical. One report of the Mercator Institute for China Studies (MERICS) – a think tank focused on policy-oriented research – provides an insightful examination of the Chinese media coverage of the Social Credit System in the first half of 2017. They found that "[n]either official nor private media fundamentally question the need for the Social Credit System” (Ohlberg, Ahmed and Lang 7). Chinese media generally approach the topic of the Social Credit System quite even-handedly, affirming the state’s opinions that it will cure societal issues, such as food security and consumer rights. Critical voices mainly focus on the technical issues of the Social Credit System, such as infrastructure and data quality, and privacy issues of corporate credit systems. Some articles raise questions surrounding the hackability and ‘objectivity’ of commercial credit scores; one mentions growing "data black markets" where hackers raise Sesame scores (Ohlberg, Ahmed and Lang 8). While Chinese media often frame the state as a trustworthy authority, there is a general tendency of distrust towards commercial enterprises such as Ant Financial that have access to vast amounts of personal data. Censorship and self-censorship might be one reason why Chinese media have been reluctant in raising critical questions towards the State. Another reason might be that commercial credit systems are currently way more common than the state-run pilots, and thus receive more critical attention. In line with the passive attitude of journalistic media, the topic has not raised much critical debate on Chinese social media either. Ohlberg, Ahmed and Lang note that social media coverage mainly consists of reposting of journalistic articles (5). Manya Koetse, a Chinese social media expert from the social trend-watching website What’s on Weibo, agrees that the Social Credit System currently is not a hot topic on the internet. On the contrary, she writes that when Sesame Credit is mentioned, it “is mostly linked to fun extras and the Chinese sharing economy.” (Koetse) To whatever extend censorship plays a role here too, it remains evident that the Social Credit System is not a topic much-discussed under Chinese citizens. Conversely, English-language journalism generally has a more dystopian view on the construction of the Social Credit System. Two comparisons pop up regularly in popular

6 media: George Orwell’s 1984 and the episode ‘Nosedive’ of the sci-fi series Black Mirror.1 Although concerns about increasing surveillance and centralisation of power are pressing indeed, these dystopian comparisons often simplify the real-world situation and provide a one-sided view. Moreover, factual information is often lacking or incorrect. Quotes by Alibaba executives are decontextualised and exaggerated through echo-chamber effects2, and many articles confuse Sesame Credit with the Social Credit System. They often frame Sesame Credit as a potential forerunner of a ‘national citizen rating mechanism’ (it is unclear if the SCS will even use a numeric scale for evaluation), while it should rather be viewed as part of one of the Social Credit System’s multiple goals: to foster the development credit economy.3 Academic literature has responded to the construction of the Social Credit System and media coverage primarily by providing an as-accurate-as-possible view on the current state of affairs. There is still little academic coverage on the topic of social credit, and there is a need for more research from various disciplines. One of the main challenges of this thesis is therefore that there is little academic literature and it is hard to tell fact from fiction. Some of the primary academic sources of this thesis were published, or yet to be published, during the time of writing.4 Rogier Creemers, one of the leading law scholars researching the SCS, translated government policy documents and published a detailed description of the current situation in May 2018. Shazeda Ahmed is an expert on commercial and state pilot projects and

1 See for example: “Big Data Meets Big Brother as China Moves to Rate its Citizens” (Botsman); “Sesame Credit, Fintech and Social Credit Scores in China” (Borak); “China: When Big Data Meets Big Brother” (Clover); “Open Sesame? China’s Social Credit Revolution Hits A Roadblock” (Perkins); “Black Mirror Is Coming True in China, Where Your 'Rating' Affects Your Home, Transport and Social Circle” (Vincent). 2 One statement is of Ant Fiancial’s technology director, Li Yingyun: “Someone who plays video games for 10 hours a day, for example, would be considered an idle person, and someone who frequently buys diapers would be considered as probably a parent, who on balance is more likely to have a sense of responsibility.” The statement was originally published by the Chinese journal Caixin in 2015, but has been copied in numerous English-language articles. See for example: “Big Data Meets Big Brother as China Moves to Rate its Citizens” (Botsman); “How China Wants to Rate Its Citizens” (Fan); “China’s “Social Credit System” Will Rate How Valuable You Are as a Human” (Galeon); “China 'Social Credit': Beijing Sets Up Huge System” (Hatton). 3 See for example: “Big Data Meets Big Brother as China Moves to Rate its Citizens” (Botsman); “China Wants to Give All of its Citizens a Score – And Those Who Fall Short Will Be denied Basic Privileges” (Denyer). 4 For my research, I attended the 16th Chinese Internet Research Conference – themed ‘Modes of Connection', in Leiden (22-23 May 2018) – which dedicated one panel to the Social Credit System. The presentations focused on advances in pilot projects and their role in the construction of the SCS. Audience questions addressed issues of clarification of rumours from the media. The presentations at the conference and the subsequent one-day workshop on the SCS show that research on social credit systems is still in an early stage that mainly approaches the SCS and its pilots descriptively.

7 Sesame Credit-related issues. She published an article at The Citizen Lab in 2017 on security considerations of Sesame Credit and, together with Mareike Ohlberg and Bertram Lang, the MERICS report on media coverage and pilot projects discussed above. Genia Kotska has recently published a quantitative study on public acceptance and user perceptions of social credit pilots. Other essential publications are Mirjam Meissner’s 2017 MERICS report on the implications of the SCS for businesses and Packin, Lev-Aretz’s article on the SCS and the right to be ‘unnetworked’, and Xin Dai’s descriptive analysis of the Social Credit System Project in China’s emerging reputation state (draft paper). The academic corpus on the Social Credit System and affiliated pilots so far has primarily approached the issues from a legal and economic perspective, as much of the research is policy-oriented. With the notable exception of Kotska’s research, user perspectives are underrepresented. Besides Hao Wang’s (unpublished) research on disciplinary techniques of credit systems through algorithmic transparency, a critical approach to social credit has lacked in this corpus. Humanities-grounded Chinese media and internet studies has so far neglected social credit-related issues. While state influence on social media platforms is well-discussed in this field – focusing on issues of censorship and contention (Poell, de Kloet and Zeng; Yang, Power), civility (Yang, Emotions; De Seta), and ‘networked authoritarianism’ (MacKinnon) – critical research on the relation between credit platforms and the Chinese government has been lacking. Jia and Winseck have, however, argued that “to better understand the Chinese internet, we must grasp not just the tight relationship between the state and business but the emergent three-way ties between the state, internet companies, and finance capital.” (Jia and Winseck 32) Instead of a political perspective on the Chinese Internet, the field must also take the view of political economy. This thesis also relates to platform studies and critical data studies, as it quite closely follows José van Dijcks analytical model to examine platforms through the lens of political economy and Actor-Network-Theory (ANT) described in her book The Culture of Connectivity. Her methodology approaches online platforms both as socioeconomic structures – focusing on ownership, governance, and business model – and as sociotechnical constructs – by analysing technology, users and content. Although Van Dijck uses her method to study how social network sites and platforms for user-generated content mediate sociality, I employ her model to analyse the power structures of the trading and marketing platform Sesame Credit, and, consequently, also its effects on sociality.

8 Moreover, platform studies, and particularly Van Dijcks book The Culture of Connectivity, has focussed mainly on ‘Western’ platforms (see also Srnicek’s Platform Capitalism and Van Dijck, Poell and De Waal’s new book The Platform Society) and could benefit from case studies of platforms in Chinese contexts. Conversely, academic and non- academic discourse should regard the social credit systems in China in the global context of increasing platformisation and datafication, and growing reputation economies. This thesis positions itself at the intersection of these academic debates – adding a critical perspective to the law and economy-dominated discussion of Chinese social credit and introducing the topic to Chinese internet and media studies – by analysing how Sesame Credit mediates power relations as a technological platform. Building on the policy-based research of social credit studies, it takes a ‘top-down’ approach of what José van Dijck calls “platform strategies” and contrasts this with a bottom-up perspective of “user tactics” (20). I use qualitative in-depth interviews with users to complement Kotska’s quantitative analysis. The main question of this thesis is “How does Sesame Credit mediate power relations between Alibaba and its users?” This research question is split up into three sub- questions: “How is Sesame Credit positioned in a network of institutional power structures of Alibaba and the Chinese government?”; “How are Alibaba’s norms inscribed in Sesame Credit as a technological platform?”; and “How does disciplinary power operate through the everyday user practices of Sesame Credit and what are the user’s motivations of use?”

Theory and methodology Methodologically speaking, this thesis follows the approach of ‘cultural analysis’ (Bal). Cultural analysis typically aims to ‘conduct a meeting’ between an object and a concept and uses close reading, derived from textual analysis, to analyse cultural objects. Cultural analysis aims to take on a critical perspective on cultural phenomena and adopts a self- reflexive stance. As cultural analysis aims for a dialogue between object and concept, the theory will be introduced – and thoroughly discussed in relation to the objects – in the chapters whenever the analysis asks for it. Hence, this introduction does not provide an in- depth overview of the theoretical framework to avoid ‘applying’ theory ‘on’ the object. It seems fit, however, to note in advance that this thesis adopts a Foucauldian conception of power to answer the above-mentioned research questions. For Foucault, power is not a quality that an entity can possess but is relational. This allows us to see how

9 Sesame Credit is positioned in a network of power relations; between Alibaba, the Chinese government, users, and merchants connected to the platform. Actor Network Theory offers a productive view on Sesame Credit’s practice of mediation of these power relations, as it recognises non-human forms of agency: the technological actors within the credit system and the platform as a whole. The methodology of cultural analysis does not come without complications. First of all, Sesame Credit is not a clearly demarcated cultural object like a text or an artwork, which makes it difficult to close read. And, as might be clear by now, academic literature is still struggling to define its characteristics and boundaries. Practically speaking, this means that this thesis frames Sesame Credit in different ways – as a technology of power, a technological platform, and an everyday consumer credit system – and close reads relevant objects, such as policy documents, the Sesame Credit application and its interface, and user experiences and motivations. Besides this, Sesame Credit is defined by another cultural object, that is the Social Credit System. As the Chinese government initiated the development of Sesame Credit as a pilot under the project of the SCS, an analysis of Sesame Credit cannot exclude the examination of its relation to the national Social Credit System. Furthermore, many parts of the Sesame Credit are ‘blackboxed’, i.e. only the inputs and outputs are visible while the inner workings remain opaque. Both on a technical level and the level of business strategies, these mechanics are hidden. This problem is unavoidable when analysing a financial platform such as Sesame Credit, as trade secrets prevent businesses to reveal these inner workings. To circumvent this problem for close reading, I resort to reverse engineering techniques – looking at inputs and outputs to uncover parts of these inner structures – and rely on existing academic literature to analyse technical aspects from the ‘outside’. To examine how users engage with Sesame Credit in everyday life, I made a research trip to Shanghai, where I stayed for four months. In this time, I used Alipay and Sesame Credit on a daily basis and spoke to multiple Chinese users of the credit system. I set up a personal Sesame Credit account for this research, which allowed me to study how my daily actions influence the score (and to try reverse-engineering the algorithmic decision making). I also performed six in-depth semi-structured interviews (Bernard 212) with Chinese users within my social circle. Of the six informants, three were male and three female, aged

10 between 21 and 27. Most came from and lived in Shanghai or other urban areas in China. The interviews were performed in English, as my Chinese is insufficient for conversations. The interviews were set in an informal setting, at their home, in a café, or over the phone, and structured like informal conversations. The interviews are audio recorded5 and transcribed afterwards. For ethical reasons the participants were noted in advance about the purposes of the research and were aware that the conversation was being recorded.6 They were also informed that the interview was on a voluntary basis and that they were not obliged to answer any questions they did not want to answer, as some questions ask for their opinion on politically sensitive topics. I also replaced their names with common given names in China to ensure their anonymity. The ethnographic study on users and usage of Sesame Credit is limited in scope, due to restrictions of time and resources. Its aim is not to provide a full ethnography of social credit in everyday life. Rather, it functions as a pilot study that identifies the most general uses and motivations. As such, it forms a base for further research on the topic. In line with this, and because of the little academic coverage about Chinese social credit, this thesis as a whole is a pilot study that explores the power relations of social credit systems. Hence, it focuses on three diverse aspects: a political economy of Sesame Credit, the technological structure of the platform, and an audience ethnography.

Chapter structure The first chapter aims to answer the question “How is Sesame Credit positioned in a network of institutional power structures of Alibaba and the Chinese government?” This chapter builds on the policy-based research of social credit studies and presents an overview of the current state of affairs of the national Social Credit System. It then frames the object of Sesame Credit within this context. I close read one policy document that describes the government’s plans most clearly and has been central to social credit studies. Then, I discuss Sesame Credit within the ‘ecosystem of connective media’ (Van Dijck, Culture) and the ‘business ecosystem’ (Moore) of Alibaba along the lines three concepts from Manuel Castells’ theory of political economy: ownership, business model, and

5 One interview is partly video recorded to follow the user’s walkthrough, as was proposed by the participant himself. 6 This research project is approved by the Ethics Committee of the University of Amsterdam.

11 governance. Building on Foucauldian theory, I argue here that Sesame Credit is a ‘technology of power’. The chapter ends with a discussion on how Sesame Credit relates to Chinese media and Internet studies. The second chapter analyses Sesame Credit through a close reading of the smartphone application and its interface. The research question is “How are Alibaba’s norms inscribed in Sesame Credit as a technological platform?” Here, I use Actor-Network- Theory to account for non-human actors that mediate power relations. I use Light, Burgess and Duguay’s ‘walkthrough method’, to first give an outline of how the system works. In their method, the researcher walks the reader through an application, focusing on the functions and features and semiotic structures. Following José van Dijck, the analysis uses four intersecting concepts: data, algorithm, interface and content. This chapter aims to explain the “implicit participation”, or the usage inscribed in the technology itself (Van Dijck, Culture 33). The third chapter is themed around the user of Sesame Credit and aims to answer the question “How does disciplinary power operate through the everyday user practices of Sesame Credit and what are the user’s motivations for use?” It focuses on the intersection between implicit and “explicit use” of the application (Van Dijck, Culture 33). Explicit use refers to the actual use of technological platforms in situ. This chapter approaches explicit users as ethnographic subjects, and their everyday usage practices and motivations for use are the objects of analysis. I analyse this through a close reading of in-depth interviews. During the interviews, I asked the participants to walk me through their everyday usage of Sesame Credit, following the guidelines of Light, Burgess and Duguay’s ‘walkthrough method’. The chapter first offers a reflection on my position as a researcher before going into the user practices and their motivations for use.

12 Chapter 1: Alibaba and the Chinese government

1.1 Introduction The question “How is Sesame Credit positioned in a network of institutional power structures of Alibaba and the Chinese government” is central in this chapter. To answer this, I first elaborate on the Chinese government’s plans to construct a national Social Credit System (SCS), under which China’s central bank initially licenced Sesame Credit as a commercial pilot. The next section focuses on the function of Sesame Credit in Alibaba’s ecosystem by analysing the structures of ownership, business model, and governance. Following a Foucauldian theoretical framework, it argues that Sesame Credit is a technology of power that mediates (financial, social, behavioural (etc.) norms to its user-base that support Alibaba’s business model. Lastly, I look at the relation between Sesame Credit and what I term an ideology of ‘credit culture’ that the Chinese government aims to establish, basing my analysis on literature from Chinese media and Internet studies.

1.2 China’s 2020 National Social Credit System This section gives an overview of the current status of implementation of the national Social Credit System. It first discusses an essential policy document released in 2014 that gives an outline of what the Social Credit System will be and its how it is to be constructed. Hereafter, I focus on the mechanisms that are already in place.

The Construction of a Social Credit System In June 2014, the State Council released the “Planning Outline for the Construction of a Social Credit System (2014-2020)” (“社会信用体系建设规划纲要(2014-2020年)" / "shèhuì xìnyòng tǐxì jiànshè guīhuà gāngyào (2014-2020 nián)") This document is the first of a series of government policy publications and is still the most comprehensive overview of what the SCS will look like. It has been a cornerstone in policy-oriented research in social credit studies. Rogier Creemers, who made a translation of the policy document, comments that the document “put forward a timetable until 2020 for the realization of five major objectives: creating a legal and regulatory framework for the SCS, building credit

13 investigation and oversight, fostering a flourishing market built on credit services, and completing incentive and punishment mechanisms” (12). The prime objective of the SCS is the promotion of sincerity on four different levels of society: it aims to make government affairs more ‘sincere’, to increase commercial sincerity, social sincerity, and enhance credibility in juridical affairs (China, State Council). The “Planning Outline” also emphasises the SCS’s dual goal of social management and economic regulation. The creation of the Social Credit System is paralleled with an ideological transformation; it goes hand-in-hand with the promotion of ‘Core Socialist Values’ (社会主义核心价值观 / shèhuì zhǔyì héxīn jiàzhí guān). On the other side, it will be an important mechanism that strengthens the Socialist Market Economy (社会主义市场经济 / shèhuì zhǔyì shìchǎng jīngjì). The Socialist Core Value System (社会主义核心价值体系 / shèhuì zhǔyì héxīn jiàzhí tǐxì) is a set of twelve moral principles – of which integrity is one (诚信 / chéngxìn; etymologically close to ‘credit’/’sincerity’) – that is introduced by the State in 2012 to fight the ‘moral decay’ of the past decades of increasing individualization. While these twelve values are being promoted in education, via propaganda and the strategic use of different media (China, State Council, Planning Outline, Part III.1), the Social Credit System will give another impulse in the moral schooling of Chinese citizens. In particular, the SCS aims to establish a ‘sincerity culture’7 (诚信文化 / chéngxìn wénhuà) and create a “thick atmosphere in the entire society that keeping trust is glorious and breaking trust is disgraceful and ensure that sincerity and trustworthiness become conscious norms of action among all the people.” (sic) (China, State Council, Planning Outline, Part I.3) On the other hand, the Social Credit System is an essential component of the Socialist Market Economy. The Socialist Market Economy was introduced in Deng Xiaoping’s economic reformations of the late 1970s and merges a planned Socialist economy with market elements. It is part of his idea of ‘material civilisation’, which he proposed alongside the concept of ‘Socialist spiritual civilisation’ (Yang, Emotions 1949). The SCS allows the State to effectively regulate the behaviour of market participants and reach industrial and technological targets (Meissner 4). As the evaluations of customers and companies will drastically influence their market positions, the SCS can infuse regulations in market

7 Rogier Creemers has translated this as ‘sincerity’ but bear in mind that it is the same word as the Socialist Core Value of ‘integrity’.

14 exchange. In other words, planning will be implemented in the market economy itself and State influence will be visible on the level of ‘liberal’ trade as credit scores become part of competition. The “Planning Outline” states that governmental sincerity is the core of the creation of the Social Credit System (part II, section 1). Without a trustworthy network of administration and honest enforcement of regulations, the implementation of the SCS on other levels will not be possible. In line with this, the SCS also aims to improve judicial credibility to assure an honest prosecutorial basis for punishment systems. The government also has an exemplary role in society; raising honesty, accuracy, efficiency in government affairs, and even transparency of policies and regulations, will be a model of sincere behaviour for the rest of society. In the commercial sector, the Social Credit System will evaluate the economic and social behaviour of companies. The State Council hopes to resolve societal problems such as issues with food security, tax fraud, violation of consumer rights, and environmental pollution (China, State Council, Planning Outline, Part II.2). Meissner’s 2017 MERICS report shows a comprehensive diagram that shows what data is used to compile credit scores and what possible consequences are (see Image 1). Input data includes information on company representatives, annual reports of corporations, and compliance with government regulations (for example internet regulations, safe work environments, tax payment, environmental impact, and intellectual property). The scores have an effect on subsidies and investment opportunities of the companies, and even travel possibilities and career opportunities of company representatives, among other things. Ultimately, the system will also incorporate real-time and remote monitoring and automated score computation (Meissner 4). In e-commerce, for example, real-time data could provide information on customer satisfaction, delivery, product quality, etc. In the transportation sector, vehicles will be tracked remotely, and in polluting industries emissions will be monitored in real- time. Meissner notes that, instead of a centralised rating organisation that compiles a single score per company, “the government plans suggest a rather diversified and decentralized market for social credit ratings” with multiple (commercial and governmental) score providers (5).

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Image 1: Dataflows of the Social Credit System in the commercial sector (source: Meissner 4) For the improvement of ‘social sincerity,’ the Social Credit System is implemented in areas of healthcare, social security, and education. The "Planning Outline" also dedicates one paragraph to the construction of a credit system that evaluates ‘natural persons’. It states that the Social Credit System will collect credit records on the economic and social lives of individuals, alongside the ratings that relate to their professional function. Moreover, the document also mentions that a system will be constructed to evaluate the online behaviour of so-called ‘netizens’ (网民 / wǎngmín). Interestingly enough, the document does not mention quantitative scoring as a method of evaluation (Creemers 13). Although the document implies that the SCS will increase surveillance on citizens and evaluate their behaviour, it is unclear if it will rate citizens with an actual ‘citizen score’. Although the mention of a credit system for natural persons and netizens is brief in this first document, further publications specify some of the consequences for individual citizens. Blacklisted people are for example restricted to hold high positions in companies or government bodies. Other imposed restrictions are on train or air travel, hotels and restaurants, conspicuous consumption travel (such as organised holidays to foreign countries and other holiday areas), high-fee schools for children of the subject, and building and renovating housing (China, State Council, Opinions).

16 In short, while the Chinese government presents the Social Credits System as a "cure-all" for a whole range of societal problems (Ohlberg, Ahmed and Lang 5), it will also be a massive technology-based system for economic and social control (Creemers 3). The SCS has two main functions: to promote financial credit and to track and manipulate citizens’ social and moral behaviour. The two are, however, not completely separated. The core of the system will be the collection of data on individuals and organisations from a multiplicity of sources, possibly also commercial ones, that will be shared among local and central government bodies. On the basis of these data and evaluations, citizens and institutions will be evaluated and penalised or rewarded accordingly.

Current status of the implementation Although the construction of the Social Credit System is still at an early stage, certain fundamental components have already been set in place (Ohlberg, Ahmed and Lang 2). So far, the primary concern has been the construction of a data sharing infrastructure and the establishment of the Joint Punishment System. Besides this, local governments and commercial actors have established pilots to experiment with the practical application of social credit systems. One significant factor of the digitisation of social management has been the introduction of the 2003 Identity Card Law (Creemers 20). The 18-digit identity cards render citizens digitally identifiable and allow for efficient data collection. It has become a universal system for digital identification and is used by different government bodies and private enterprises. Together with the regulations on real-name authentication requirements in online environments, such as social media and other account-based systems, and in mobile phone registration, the introduction of the ID code made it possible to connect multiple data points and store this information efficiently (Creemers 21). For the collection and sharing of data for the Social Credit System, the government established the ‘National Credit Information Sharing Platform’ (全国信用信息共享平台 / quánguó xìnyòng xìnxī gòngxiǎng píngtái) in October 2015 (Meissner 6). This has been the central platform that receives data from multiple ministries and other government bodies, such as the Peoples Bank of China and the National Development and Reform Commission (NDRC) that both lead the implication of the SCS. The Information Sharing Platform is the

17 back-end data provider for the information platform ‘Credit China’ (信用中国网 / xìnyòng zhōngguó wǎng). This website, built in collaboration with Baidu, provides information about the Social Credit System itself and makes public credit-related information about companies and individuals (Creemers 21). The National Enterprise Credit Information Publicity System (国家企业信用信息公示系统 / guójiā qì yè xìnyòng xìnxī gōngshì xìtǒng) is another platform that publicises information – particularly on companies – that is backed by the Information Sharing Platform. These practices of publicly ‘naming and shaming’ are part of a punishment system that is connected to the Social Credit System. Although these databases are not yet up to their full potential, they form the basis of a data sharing infrastructure that could in the future be the core of the SCS (Ohlberg, Ahmed and Lang 11). The Joint Punishment System (联合惩治体系 / liánhé chéngzhì tǐxì) is another element that has already been set in place. Jointly established by 45 collaborating state bodies, it is a blacklisting system that currently primarily lists citizens that resisted court orders and companies that do not conform to the law or regulations (Ohlberg, Ahmed and Lang 10). Punishments that follow for blacklisted individuals and companies include restrictions in economic opportunities, constraints in holding high-positions in certain organisations, and limits in conspicuous consumption (Creemers 15). The latter encompasses restricted access to first-class air travel and high-speed trains, luxury hotels and restaurants, holidays to foreign countries and fee-paying schools for the entrant’s children. As these blacklists are shared with local governments and even private enterprises, the restrictions are imposed everywhere. Alibaba’s Taobao and Tmall, for example, deny blacklisted entrants to make luxury purchases (. Some local governments have already incorporated the blacklist system as a punishment mechanism for minor offences. The city of Ningbo, for example, has blacklisted fare-dodging individuals and Shenzhen installed facial-recognition technologies at zebras and reports repeatedly caught individuals (Creemers 17-18). In several cities and districts, local governments are experimenting with social credit information systems. The NDRC and PBoC have appointed 43 municipalities to construct pilots for the national SCS. Many of these trials have developed their own rating system and are testing punishment mechanisms for blacklisted citizens and companies. Rongsheng, one of 12 selected ‘model cities’, has introduced a scoring-based system in which individuals are

18 rated on a scale that goes up to 1000 points (Ohlberg, Ahmed and Lang 12; Creemers 19). The scores are then categorised into six categories (AAA to D) with appurtenant consequences. Shanghai has released a smartphone application called ‘Honest Shanghai’ (诚 信上海 / chéngxìn shànghǎi), in which users can register with their national ID number and facial recognition (Ohlberg, Ahmed and Lang 12; Creemers 18). The application then computes a score based on government documents and rates the citizen with one of three categories: very good, good, and bad. The app also shows the ratings of local companies on a map marked with green, yellow, and red smileys.

Image 2: In-store certificate of Honest Shanghai quality test. The People’s Bank of China also encouraged eight tech companies in 2015 to develop commercial pilots for the national SCS and to stimulate the credit economy and increase financial inclusion. Citizens that traditionally lack credit records could benefit from such credit systems as they gain financial opportunities from these commercial credit systems. Among these pilots are Tencent, WeChat’s parent company, Baidu, and Alibaba’s subsidiary Ant Financial that developed Sesame Credit. The central bank initially gave the companies six months to develop a commercial credit-reporting system (Creemers 22). According to the PBoC, however, these commercial trials were not a success; in 2017 the central bank refrained from granting official Social Credit System pilot licences to any of the commercial enterprises. The main reason was a conflict of interests: the priorities of the companies lay at developing a credit system that supported their own core business, such as

19 e-commerce or insurance (Creemers 24). The vision of the People’s Bank, however, was to establish a centralised credit reporting system. Due to the direct market competition of the companies, they would not share their proprietary data, creating a fragmented credit economy (Ohlberg, Ahmed and Lang 12). This also led to complains on behalf of the central bank about the ability to indicate accurate financial credibility of the systems. Although the eight companies included some of the largest data-owners in China, their data generation is restricted to their relative userbases and business areas (Creemers 24-25). Other criticisms were that the companies did not protect user privacy (Ohlberg, Ahmed and Lang 12) and that due to their commercial interests, the companies could not inhabit an independent third party-position (Reuters Staff). In an effort to solve these problems, the National Internet Finance Association (NIFA) founded a credit scoring bureau called Baihang together with the eight companies. The NIFA is a government organisation originally initiated by, and still under the administrative control of, the People’s Bank of China (Creemers 25). The government body owns the majority of equity stakes with a percentage of 36%, while the rest of the shares are equally distributed under the eight companies. As a semi-commercial credit scoring bureau, Baihang complements the central bank’s Credit Reference Centre, which is its main governmental credit authority. The credit union received a three-year licence from the People’s Bank of China, which allows the central bank to keep a close watch on the commercial credit system pilots (Creemers 25). It is however uncertain how this partnership is going to unfold in the coming years. If the cooperation lasts, the government might be able to unify the data from the eight companies to support the national SCS. The cooperation might however also break due to the competing commercial interests of the private companies. The future relationship between the national Social Credit System and Sesame Credit thus remains ambiguous, but it is certainly an important development to follow as the implementation of the SCS advances. One major factor in this relationship will be the commercial interests of Ant Financial. These are currently for the most part in line with the goals of the government, but this could change if the Baihang collaboration fails. The next section elaborates on these interests of Ant Financial and its superior Alibaba.

20 1.3 Alibaba’s business ecosystem As the previous section has shown, Sesame Credit is connected to the political context of the Social Credit System. But although the Chinese government had originally initiated the development of commercial social credit pilots, Sesame Credit remains a consumer credit system owned by the private enterprises Alibaba and Ant. This section analyses the commercial and organisational (infra)structures of Sesame Credit within Alibaba’s ‘business ecosystem’ (Moore), and in the larger ‘ecosystem’ of connective media in China (Van Dijck, Culture). To do so, I use three concepts from Manuel Castells’ theory of political economy: ownership, business model, and governance. “Proponents of political economy”, remarks José van Dijck, “[…] regard platforms and digital networks as manifestations of power relationships between institutional producers and individual consumers.” (27) It is precisely the commercial and institutional power relations that this section aims to address.

Ownership: Alibaba’s e-business ecosystem Ownership is perhaps the most crucial aspect in defining institutional positions of power. In this light, it no surprise that the government is cautious in granting credit scoring licences to any one of the eight companies. In the Baihang joint credit bureau, the PBoC, via the NIFA, retains its position of power as it has the highest equity stakes in the enterprise. In this way, the central bank also maintains its control over the commercial initiatives. In Sesame Credit, the most significant stakeholders are Ant Financial and, by extension, the Alibaba Group. Ant Financial started as the payment platform Alipay to manage online payment on the Alibaba.com e-commerce website. In 2015 Alipay changed its name to Ant Financial as it began offering multiple financial services besides the payment platform. Ant Financial now also incorporates Ant Fortune, a wealth managing service; MYbank, a private online bank for micro-enterprises; and the credit scoring system Zhima Credit (Ant Financial). As such, Ant Financial grew out to be the financial arm of the Alibaba Group. Although Ant Financial largely operates as an autonomous enterprise, Alibaba has a 33% equity stake in the company (Alibaba Group and Ant Financial). In fact, Alibaba itself, which started as a business-to-business (B2B) trading website in 1999, has grown into one of the largest Internet-based conglomerates in China. Over the last two decades, the corporation has expanded its commercial imperium through takeovers

21 and partnerships. It founded the popular business-to-consumer (B2C) e-commerce website Taobao.com, partnered up with Yahoo! and acquired the Yahoo! China web portal, and founded the cloud computing company Alibaba Cloud (Huang, Hu and Lu 29). Today, the Alibaba Group leads an expanding interconnected network of companies that operate interdependently for mutual benefit. Jia and Winseck also note, that the Chinese government also has a direct influence in Alibaba as Alibaba has one government official in its board of directors (46). As the board decides on the management of the company, government goals can be implemented in the business strategies of Alibaba. Based on James F. Moore’s conception of the ‘business ecosystem’, Huang et al. argue that Alibaba has evolved into an ‘e-business ecosystem’. They define the term as “an organic ecosystem that is made of enterprises and organizations with close relations, using the internet as a platform to make competition and communication through virtual alliance, sharing resources, and making full use of their advantages beyond geographic limits” (27). By clustering multiple companies together and covering multiple sections of the market, Alibaba manages to compete with other e-business ecosystems such as eBay, which withdrew from the Chinese market in 2006, and Tencent. Much of the internal structure of Sesame Credit depends on the relationship with other platform companies. Ownership, bonds, and competition play important roles in how it works as a system of production (Van Dijck, Culture 36). On the back-end of the credit scoring system, this means that user data that Sesame Credit uses to compute scores are derived from Alibaba-owned or related enterprises. The score that is produced is thus based on behaviour on e-commerce websites, interaction on social media, and credit history (etc.). On the other side, it also means that Sesame Credit is applicable to many other companies and their services. Hight Sesame scores can for example waiver deposits on the bike-rental platform Ofo (Yu). There are even other platforms that incorporate the Sesame score into their service. The P2P services platform Daowei (到位 / dàowèi) allows only users with a score higher than 650 to offer services on the platform while requesting a service requires a minimum score of 600 (Schoenmakers). Baihe (百合 / bǎihé), an online dating platform, also encouraged users to show their Sesame score (Creemers 23). In short, looking at ownership shows that the Alibaba Group, as a ‘leading species’ (Moore) in the e-business ecosystem occupies a central position of power. Still, the

22 companies coevolve with each other in a complex, relatively decentralised, manner driven by mutual interests. The distribution of data and the applicability of Sesame Credit is built on this institutional infrastructure. Sesame Credit, as a system of production, relies on, and supports, other companies in the Alibaba e-business ecosystem.

Business model: ‘Meet, work, and live at Alibaba’ Alibaba’s vision is to create an online space for consumers and businesses to ‘meet, work, and live at Alibaba’ (Alibaba Group). It provides a platform for users, consumers, merchants, and businesses to interact socially and commercially. Small businesses can use the digital infrastructure of the Alibaba Group to connect with other companies in the ecosystem and gain a larger userbase. One example of this is Alibaba’s digitisation of traditional retail under the name of ‘New Retail’8. Small retail businesses can easily join the ecosystem and tap into its infrastructure by conducting part of their business via Alibaba-connected platforms, such as sales via Taobao and payments via Alipay. Also due to Jack Ma’s guru-like status in the e- business economy, many companies gladly follow his ideas for future developments. Thirdly, the Alibaba Group website explains that the company strives to make Alibaba products and services central to the everyday lives of their customers. It is clear from this vision that Alibaba aims to bind companies and consumers to their ecosystem and to increase user experience. These two components, gaining ecosystem members and enhancing user experience, go hand-in-hand as they amplify network effects. For example, the user experience improves as more businesses are connected to the Alipay platform. Conversely, the more people use Alipay; the more businesses join the platform. As such, platform companies have a natural tendency towards monopolisation (Srnicek 45). Similarly, Sesame Credit also enhances the network effects of the Alibaba ecosystem. On the one hand, it encourages users to interact with Alibaba-affiliated platforms such as Taobao or Tmall, as this increases Sesame scores and grants them privileges. In fact, many of these rewards also encourage users to purchase more goods and services that are related to Alibaba. This way, Sesame Credit creates behavioural feedback loops that steer users towards staying within the ecosystem. This increased user traffic

8 For an explanatory video on ‘New Retail’: https://www.youtube.com/watch?v=336YkwayCD4

23 attracts businesses to connect to Sesame and offer their products and services via the credit platform and join Alibaba’s ecosystem. Jie Guo and Harry Bouwman take an ecosystem view on the Alipay’s mobile payment platform (of which Sesame Credit is part) and focus on the dependency of actors in the ecosystem. They show that merchants highly depend on the m-payment platform as it provides, among other things, an increased customer base, cheap transaction management, and an IT infrastructure (69; table III). Many merchants and businesses do not have the resources to develop their own mobile payment infrastructure or cannot compete with payment platforms giants such as Alipay. As such, for many single merchants, it is better to join the platform than to compete against it. Conversely, Alipay also depends on merchants as they provide their products. This dependency is, however, less fundamental as Alipay’s platform relies on a multitude of suppliers. Within the Alipay ecosystem, Sesame Credit enhances trust in the commercial relationship between consumers and companies in the Alibaba ecosystem and, as such, further amplifies network effects. Indeed, Guo and Bouwman note that risk management is another element of merchant dependency on Alipay (69; table III). As a financial credit indicator, Sesame Credit reflects the customer’s ability to pay back borrowed money or products/services paid on credit. And, not unimportantly, it also reflects their loyalty to the Alibaba ecosystem. This way, Sesame Credit is a mechanism that binds consumers and companies to the Alibaba e-commerce ecosystem and strengthens their relationship. By providing a credit and payment infrastructure between consumers and companies, Sesame Credit and Alipay position themselves strategically between two interacting parties. This makes both companies and consumers dependent on their financial credit infrastructure. Furthermore, it allows platforms to extract data from this interaction and for the improvement of their own services – such as a more accurate Sesame score – and other services in the ecosystem of Alibaba.

Governance: Sesame Credit as a technology of power José van Dijck notes that “[t]o analyze the governance structure […], one needs to understand how, through what mechanisms, communication and data traffic are managed.” (Culture 38; emphasis in original) She points to contractual mechanisms such as the end-

24 user license agreements (EULAs) or terms of service (ToS). Indeed, Sesame Credit, and by extend Alipay, use these contractual mechanisms to secure the rights and obligations of platform users (i.e. companies and consumers). This way, Sesame Credit gets legal access to personal data on which it bases the credit score. Technical mechanisms like algorithms and protocols, which will be the topic of the next chapter, play important roles in automated forms of governance. It is through these mechanisms that important technical decisions are made, such as how certain data influences the score and to what privileges the user has access to. Van Dijck rightly points out that structures of governance touch upon social norms (e.g. privacy, property, and proper behaviour etc.) and often contest and (re)structure these social norms (38). Sesame Credit’s central mechanism of governance is, however, the score. Through the score, Sesame Credit regulates its user-base by granting or denying individual users access to certain privileges. By doing so, the credit scoring mechanism fosters behavioural patterns that are in line with Alibaba’s business model, i.e. increasing consumption and promoting the use of connected platforms and services. As the credit scoring system decides over everyday possibilities, such as renting a bicycle, and financial opportunities, such as borrowing money, it is a mechanism that subjects users to forms of discipline. For Foucault, discipline targets the behaviour of individual bodies (Meshes 160). Jeffrey T. Nealon notes that “discipline works on individuals precisely through the more efficient means of targeting their potential actions, their capacities: literally what they can – and can’t – do” (31). The score in Sesame Credit is a deciding mechanism in what actions the individual can and cannot undertake. Discipline does, however, not work solely through the restriction of certain actions. It is also a productive power that promotes actions and desires (Nealon 24). Through granting access to certain services, Sesame Credit promotes the use of Alibaba-connected services. It also frames services and privileges inaccessible for users with lower scores as objects of desire, encouraging users to increase their score (for example by purchasing products on Alibaba-connected e-commerce websites). Sesame Credit is a panoptic environment that monitors the user’s online and consumer behaviour, social connections and personal characteristics. Through the score, credit scoring is a mechanism that provides evaluative feedback on the user. It communicates this in terms of a binary ‘good’ and ‘bad’ influence on the score. For

25 individual users, the score is thus an indicator of how they should behave commercially, socially, or otherwise. Through its evaluative score, Sesame Credit thus also promote practices of self-discipline and self-surveillance as it urges users to adjust their behaviour to their score. Although for Foucault discipline is practised in enclosed institutional environments, like schools, factories, prison, and the army, Sesame Credit brings these disciplinary techniques into the everyday lives of users (History 262; Meshes 159-160). This way, the slogan ‘Live at Alibaba’ takes a quite literal form. In short, Sesame Credit is a technology of power in Alibaba’s business ecosystem: a mechanism that “determine[s] the conduct of individuals and submit them to certain ends or domination” (Foucault, Technologies 18). As a technology of power, Sesame Credit produces a certain normativity: norms of consumption, sociality, behaviour, etc. These norms, fuelled by Alibaba’s business goals, are implemented in the everyday lives of Sesame Credit users.

1.4 China’s credit culture The previous subchapters have shown that Sesame Credit functions as a private credit rating system, relatively distinct from the emerging national Social Credit System. However, as Baihang shows, close ties remain between Sesame Credit and the People’s Bank of China. Returning to the question “How is Sesame Credit positioned in a network of institutional power structures of Alibaba and the Chinese government”, this subchapter further explores Sesame Credit as a platform on the Chinese internet. Although there are no definite answers yet on the collaboration between Internet companies and the Chinese government concerning social credit, there already is a close relation between the two. Social media websites like Sina Weibo and WeChat carry out the State’s censorship regulations, and, as discussed above, Alibaba restricts blacklisted civilians from buying luxury goods. The government’s power over the Internet is further exemplified by the nation-wide block of thousands of foreign websites (Poell, de Kloet and Zeng 3), also known as ‘The Great Firewall of China’. The internet in China is thus a combination of State, companies and internet users.

26 Rebecca MacKinnon coined the term ‘networked authoritarianism’ to describe the Chinese government’s control over the Internet. She argues that this control is manifested in four different ways, corresponding to four deliberative spaces in Chinese cyberspace identified by Min Jian: central propaganda spaces, government-regulated commercial spaces, emergent civic spaces, and international deliberative spaces. The government’s influence on Alipay and Sesame Credit can be understood in relation to the second category: the platforms are “owned and operated by private companies but subject to government regulation” (36). She notes, however, that networked authoritarianism has two sides: “single ruling party remains in control while a wide range of conversations about the country’s problems nonetheless occurs on websites and social-networking services.” (33) As such, citizens enjoy a greater ‘sense of freedom’ as Internet platforms allow them to talk about political issues, while the government still controls. Xueqing Li, Francis L. F. Lee and Ying Li also point to the dual impact of social media platforms; on the one hand, social media promotes political awareness and civic culture, while on the other hand, it enlarges support for the political system. This double view on China’s Internet control resonates through Chinese Internet and media studies, many authors arguing that although the Internet is heavily surveilled and controlled by the State, there still is room for humour (Poell, de Kloet and Zeng) and contention (Yang, Power). Guobin Yang argues that the Chinese government recently turned towards proactive and preventive methods of governing the Internet that produces a ‘positive energy’ online (1946). At the core of this is the idea of ‘wénmíng’ (文明), which can mean both ‘civilization’ and ‘civility’ and is one of the twelve Socialist Core Values. Yang explains that “[a]s civilization, wenming operates as an ideological discourse that legitimates the governance and administration of society. As civility, it functions as a strategic technology and tool for governance and self-governance, including the governance of the Internet.” (1946; emhasis in original) Wénmíng thus operates as a soft power that is fostered by the State but also performed by non-State actors like platform companies and Internet users. It is in this light that we can understand how the Chinese government promotes xìnyòng (信用), or ‘credit’. On the one hand, the government aims to establish, what may be

27 called a credit culture.9 I use ‘credit’ here in the Chinese sense, that includes, besides financial credibility, also moral values like sincerity, trustworthiness and integrity and is etymologically close to the Socialist Core Value of integrity (诚信 / chéngxìn). This way, China’s credit culture is similar to Yang’s first function of wénmíng: it is “an ideological discourse that legitimates the governance and administration of society” (1946). At the centre of this ideological discourse is the belief that these values can be calculated objectively. Van Dijck calls this an ideology of ‘dataism’:

dataism shows characteristics of a widespread belief in the objective quantification and potential tracking of all kinds of human behaviour and sociality through online media technologies. Besides, dataism also involves trust in the (institutional) agents that collect, interpret, and share (meta)data culled from social media, internet platforms, and other communication technologies. (Datafication 198; emphasis in original)

In the name of increased societal ‘credibility’, the Chinese government is able to increase surveillance practices and personal restrictions. In light of Foucault’s biopower, we can see how credit is thus used to increase the living standards – hence biopower – of the (normative part of the) population, while at the same time targeting individuals. Indeed, credit is also used as a "technology and tool for governance and self- governance" as the previous section shows (Yang, Emotions 1946). Sesame Credit is one of these technologies of power that disciplines individual users by evaluating their behaviour and rewarding or punishing them according to their score. Although Sesame Credit mainly mediates Alibaba’s business goals, it is also positioned in the credit culture that the Chinese government aims to establish. It is part of the ideological apparatus that assumes that societal values such as credibility, sincerity, and trustworthiness can be measured and calculated into a numerical scale of ‘credit’. As such, Sesame Credit combines hard forms of power – through the discipline of users – with soft power; it promotes the ideology of a credit culture.

9 I derived the notion of ‘credit culture' from the Chinese government's call to promote a ‘sincerity culture' in the "Planning Outline" as the words for ‘credit' and ‘sincerity' are strongly connected in the Chinese language.

28 Chapter 2: Sesame Credit as a Technological Platform

2.1 Introduction After establishing that Sesame Credit is a technology of power that produces norms that are fuelled by Alibaba’s business model, this chapter asks the question “How are Alibaba’s norms inscribed in Sesame Credit as a technological platform?” By conceptually framing the credit scoring system as a technological platform, this chapter adopts a double-sided view of Sesame Credit as a space that facilitates interaction and mediates this interaction through the network of technological actors. As a platform, Sesame Credit is positioned between multiple actors and facilitates their interaction: Alibaba and Ant Financial, users, third-party merchants, and, although in a less direct way, the Chinese government. Tarleton Gillespie notes several different meanings of the notion of platform (Politics 349-350): from its origin in architecture, the notion of platform implies a figurative ‘raised surface’ which multiple parties can claim. As such, although platforms seem like neutral and open spaces, they empower whoever stands on it. This way, a platform also bears a political meaning: it is the stage from which one can make a political statement. The meaning of the term platform also has a computational dimension: it denotes the technological "infrastructure that supports the design and use of particular applications" (Gillespie, Politics 349). Nick Srnicek also notes that the positioning of platforms between different actors, and by facilitating the digital infrastructure that allows for their interaction, platforms have the privileged access to record these interactions (44). It is in these multiple meanings of the term that this chapter views Sesame Credit as a technological platform that mediates, monitors, and modifies social interaction. I use Latour’s Actor-Network-Theory (ANT) to analyse the technological actors that operate within Sesame Credit. The main reason for this is that ANT shows how power relations are mediated, instead of merely accepting them as social facts. In his book Reassembling the Social: An Introduction to Actor-Network-Theory, Latour criticises sociologists who rely on ‘social forces’, such as ‘power’, as an explanation for states of affairs: “power, like society, is the final result of a process and not a reservoir, a stock, or a capital that will automatically provide an explanation” (Latour 64). Rather, ANT aims to

29 explain how social ties, and with that power imbalances, are constituted and composed in the first place. This way, this chapter interrogates and nuances the broad power imbalances sketched out in Chapter 1. As is often celebrated, ANT recognises the agency of non-human entities. By accepting them as “participants in the course of action”, ANT allows a perspective on how Sesame Credit, and its technological components, shapes the interaction between Alibaba, users, merchants, and the government (Latour 71; emphasis in original). This opens up the discussion on the seeming neutrality of platforms hinted at earlier; although Sesame Credit might look like an intermediary that facilitates interaction, it is a mediator that transforms, translates, distorts and modifies it (39). With ANT’s recognition of non-human agency, an ethical problem emerges: as humans are not the only accountable actants, responsibility and accountability diffuse over the network of actors. Arjun Appadurai addresses this problem of new materialism and suggests to focus on what he calls ‘mediants’, rather than on actants. He grounds his idea in Deleuzian assemblage theory (Deleuze and Guattari) and the concept of dividuality – the idea that human subjects are divided into, and composed of, a multiplicity of monads, molecules (etc.) that are temporarily and non-hierarchically in association with each other (Deleuze 5).10 A mediant, he explains, is a “dynamic assemblage of the human dividual that is available to blend with and catalyze other nonhuman mediants (and actants) to produce effective and durable patterns of assemblage” (232). What is at stake here is the mediation of normativity through the material assemblage of human and non-human dividuals. For example, we can identify as mediant the assemblage of Sesame Credit’s algorithms, the parts of the human programmer devoted to programming, the computer and screen the programmer works on, etc. This way, Appadurai’s theory allows accepting the agency non-human actants, while putting particular emphasis on the (dividual) human agency in the assemblage. This chapter focuses on several non-human actors. What we must take from Appadurai’s theory is that these non- human actors are mediating (Alibaba’s) normativity through their association with human actors, such as programmers, designers, CEO’s. They are not autonomous actors in and of themselves but come to do what they do (partly) through human intentionality.

10 This theory will be elaborated on later in this chapter, on page 44.

30 The third reason why Ant is suited to analyse Sesame Credit as a technological platform is that as ANT focuses on relations between actors, it provides a solid theoretical basis to map out the technological infrastructure of Sesame Credit. José van Dijck differentiates between five technological components that make up the infrastructure of a platform: data, algorithm, protocol, interface, and default. What makes these components actors, and accordingly constitutes the infrastructure, is the momentary connections between them and actors outside the technology (e.g. content and users). It is precisely the relational quality of these technological actors that makes them function as a digital infrastructure. Protocols are responsible for the distribution of data as they “are formal descriptions of digital message formats complemented by rules for regulating those messages in or between computing systems.” (Van Dijck, Culture 31) Protocols govern the flows of data between Sesame Credit and other systems and thus play an essential role in the computation of the score.11 Defaults are “settings automatically assigned to a software application to channel user behavior in a certain way” (Van Dijck, Culture 32). While for many platforms default settings are important mechanisms for the management of user behaviour, Sesame Credit mainly does this through its content (the score and advertisements) and interface. There was, however, one controversy at the start of 2018 about a default setting. An article of the South China Morning Post (owned by Alibaba) reports that the ‘Alipay Annual User Footprint Report’, which allows users to look up how often they have used Alipay in the previous year and for what purposes, “had a box that was checked by default, consenting to the ‘Zhima Credit Service Agreement’” (Zen). According to the article, Alibaba apologised for the ‘mistake’ and removed the opt-in feature from the User Footprint Report immediately. Apart from this example, however, default settings do not play a significant role in Sesame Credit’s construction of norms. And although protocols are significant actors in the technological infrastructure of Sesame Credit, they are not the primary sites through which disciplinary power operates. As such, I focus my analysis on the other three technological actors: data, algorithm and interface.

11 For a further discussion on control and agency in technological networks, see Galloway and Thacker

31 The analysis of algorithms and data poses a methodological problem as algorithms and data sources and flows are blackboxed. Grounding my analysis in the academic corpus of critical data studies, I am, however, still able to make claims about what algorithms and data do as actors within a technological network, rather than how they work exactly in the case of Sesame Credit. Lastly, following Van Dijck, the ANT approach on Sesame Credit taken here complements the political economy-view on Sesame Credit within Alibaba’s ecosystem described in the previous chapter. It analyses Sesame Credit as the technical counterpart of Alibaba’s commercial ideology. As we will see, the technological infrastructure mirrors the political-economic goals in many ways. José van Dijck identifies three important actors in platforms as techno-cultural constructs: technology (which subdivision is mentioned above), content, and users/usage (28). While this chapter focuses on the former two, the next chapter interrogates more deeply the latter. Notwithstanding, users, and the other non- technological participants in the interaction, will be present in this chapter as actors, as their actions are ultimately part of the social credit assemblage. To base this analysis on empirical data, I close read a Sesame Credit account set up especially for this research (although it is based on my own personal information and everyday use). I roughly follow the ‘walkthrough method’ proposed by Ben Light, Jean Burgess, and Stefanie Duguay. As they state it, “[t]he walkthrough method is a way of engaging directly with an app’s interface to examine its technological mechanisms and embedded cultural references to understand how it guides users and shapes their experiences” (2). The walkthrough method thus has the same dual focus of technological infrastructure and its social and cultural implications. Furthermore, the method borrows concepts from political economy and ANT, so it functions here as the empirical methodology to research Van Dijck’s microsystems.

2.2 A technical walkthrough of Sesame Credit This section aims to walk the reader through the Alipay application and provide a general idea of how the credit system is used.12 The walkthrough functions as the “central data-

12 The walkthrough method was started on 23 August 2018 and uses Alipay version 10.1.30 for iPhone 6S in Chinese. The app might be different on other smartphones and changes with every software update. It must also be noted that I am not a Chinese citizen and the app might be different, or respond differently to my

32 gathering procedure” of this chapter and involves me “engaging with the app interface, working through screens, tapping buttons and exploring menus” (Light, Burgess and Duguay 11). In this process, I made ‘field’ notes and recordings such as screenshots. Although interpretations about the interface, usage, etc. will be given in this walkthrough, the actual analysis of these elements will follow in the next section. Special attention will be given to what Light et al., drawing on ANT terminology, call ‘mediator characteristics’. Mediator characteristics “provide indications of how the app seeks to configure relations among actors, such as how it guides users to interact (or not) and how these actors construct or transfer meaning.” (11) Among these are: user interface and arrangement; functions and features; textual content and tone; and symbolic representation. Preliminary attention towards these mediator characteristics focuses the walkthrough on elements that will be used in the ANT analysis. Light et al. distinguish three stages of walking through a smartphone application: registration and entry; everyday use; app suspension, closure and leaving.

Registration and entry Light et al. mention that the registration stage offers insights into an app’s governance, expected use, and vision (12). Indeed, to sign up for the Alipay wallet service it is necessary to accept the Alipay Service Agreement, Alipay Privacy Policy, and the Taobao Service Agreement. Herein, Alipay secures its right to collect certain data and exchange it with other entities. Upon registration, a phone number also must be provided to activate the account. Hereafter, a screen pops up that asks access to location services. As some functions might not work without GPS tracking, this pop-up nudges users to accept this request. Hereafter, the user arrives at the home screen of Alipay (Image 1). Before using any of the functions, however, the application requests the user to set up a 6-digit password for the Alipay account and asked permission to use fingerprint. Sesame Credit is one of the multiple services offered in the Alipay wallet (see Image 1). Applying for a credit score requires to go through an additional registration sequence. More specifically, Sesame Credit requires real-name authentication. When applying, the

behaviour, then for Chinese citizens. During my research the design of the application changed in an update of the Alipay Wallet app. Main characteristics of the design remained the same, as did the most important functions and features.

33 application asks for country, real name, document type (Chinese ID card, passport, etc.), document number, and bank account number. A verification code is then sent to the Alipay accountholder’s phone. Hereafter, the user must upload a picture of his/her identification document to verify the information. Within 24 hours, the information is verified, and the user has access to his/her Sesame Credit score. This way, Sesame Credit obtains crucial identity documents and information about the users and folds them into their system.

34 Everyday use When opening Sesame Credit for the first time, it showed the ‘moderate’ score of 552 (Image 2). Tapping on the score opens a new page that shows the five categories out of which the score is composed: credit history (e.g. from borrowed loans); contract fulfilment capacity (e.g. electricity and gas bills); personal characteristics (age, education, occupation, place of residence, etc.); behaviour and preference (e.g. usage of the Alipay app and shopping behaviour on Alibaba-owned commercial websites such as Taobao); and interpersonal relations (based on connections in the Alipay app) (Image 3). Tapping on each icon reveals a small explanation of the category. To get a sense of the algorithmic computation, I performed some of the actions proposed by the explanations. Alipay became my personal default payment method in daily transactions; transferring money to small shop owners or friends by scanning their QR- codes, paying in-store by scanning my own code, and receiving money from friends with my personal QR-code (these three basic payment features correspond to the first three icons on the top in Image 1). I also tried to put money on Alipay’s money market fund Yu’e Bao, but it was restricted to Chinese citizens only. Instead, I raised my balance to 800 Yuan (about 100 Euro). I connected a Shanghai metro application to Alipay which allows me to check-in with my phone and automatically deducts the fee from my Alipay account. I also started experimenting with online shopping: I bought an 80¥ (€10,-) iPhone case on Taobao and ordered food via Kao’bei (third menu section of Alipay; see Image 1). Besides using Alipay in daily life, I also submitted as much personal information in Sesame Credit as possible (first option in image 6). I provided information on my education, company email address, and I linked Sesame Credit with LinkedIn (which allows Sesame Credit to access data the business platform). The application colours a star for each filled in section. I could not link with any of the other (Chinese) business platforms, as I am not registered, and could not fill in the information about a personal car and property as I don’t own them. The driver’s licence section was for Chinese citizens only. I also tried to sign up for the Alipay credit functions Ant Credit Pay, which allows the user to pay with borrowed money and return it later (similar to a credit card), and Ant Cash Now, which allows the user to borrow money. Information from these functions directly feed into the ‘credit history’ section of Sesame Credit. However, as I am not a Chinese

35 citizen, I was unable to sign up for these functions. Next chapter will show how many users interact with Sesame Credit mainly through these Alipay functions. Unfortunately, my score was not immediately affected by any of my actions. Only a month later, I discovered that my score had increased to 555, and again a month later, to 559. Updating the score once per month is thus a blackboxing technique used to cover up which actions influence the score. The score was thus too low for many offers and services, such as borrowing bikes and cars without paying deposit (see Image 5; the minimum score needed is depicted below each icon). I did try to borrow a power bank (to charge a phone) through Sesame Credit once. Scrolling down the home screen reveals a map on which umbrellas and power banks are located. I went to the café where the charging machine was located and unlocked one power bank (Image 7). Unfortunately, my score was too low to borrow it for free, so I had to pay 1¥ (€0,10) for an hour of use. Tapping through the menus gives a good sense of the further possibilities offered in Sesame Credit. The second menu section (Image 4), is where the main advertisements and offers are exhibited. The three icons depict categories like borrowing money, transportation, communication.

Image 7: Borrowing a power bank

36 2.3 Disassembling Sesame Credit: technology and content Data Other than traditional credit scoring services, Sesame Credit uses Big Data analytics as a basis of its risk prediction. By accumulating data about users and small businesses in vast volumes, multiple qualitative varieties and with the velocity of real-time tracking, Big Data analytics enables Sesame Credit to find correlations between information traditionally seen as ‘non-financial’ and financial credibility. Sesame Credit relies less on financial information that many individuals lack and can serve a bigger part of the population. This way, Sesame Credit strives for more ‘financial inclusion’, an aim also of the government, while simultaneously enlarging their market. To accumulate these massive amounts of data, Sesame Credit relies on a vast network of data sources. Yanan Zhao states that the information used for Big Data analytics and computing scores can be grouped in three categories, according to their origin: information that is users themselves submit, data from Alibaba-owned companies and services, and data from third parties (Zhao 562). Most of the personal data collected in the Alipay application is self-submitted by users. We have seen that when registering for Alipay, basic personal information, like phone and bank account number, are required to submit to get access to the platform. The app also asks access to location services to enable certain features. Sesame Credit, in addition, asks for real-name authentication and a copy of the user’s national ID card. Furthermore, other personal information such as information on occupation, education, properties, etc. can be provided voluntarily. The second source of data for Sesame Credit is data from the Alibaba-owned and partnered companies and the Alipay platform itself. Alipay’s financial infrastructure covers many financial services previously monopolised by banks. Besides on and offline payment, salary, savings, investment, bills, and credit management are among the financial services provided by Alipay. As such, Sesame Credit has access to much financial information traditionally used to calculate one’s financial credibility. On top of this, Alipay offers many services within the Alipay app that generate data, such as buying train and air travel tickets, entertainment ticketing, and commenting on products via Kao’bei. Besides this, Sesame Credit receives data on consumer behaviour and

37 preference from other Alibaba-owned platforms, such as the e-commerce websites Taobao and Tmall, where Alipay is of course the default payment method. The application also has a flourishing social media section, which allows to connect with friends, follow businesses, and receive payment notifications. It has an instant messaging service and a newsfeed section called ‘Moments’. Although the Sesame Service Agreement states that Sesame Credit will not read the content of messages and social media posts, ‘interpersonal relationships’ is one of the categories of data that Sesame Credit uses to calculated scores (Sesame Credit). Thirdly, Sesame Credit uses data from third parties. Although it is unclear to what third-party databases Sesame Credit has access, Yanan Zhao notes that information from external cooperation agencies includes "user’s income, deposits, securities, commercial insurance, real estate information and taxable amounts” (563). Sesame Credit also receives personal information from government bureaus (S. Ahmed). Although this would primarily include information such as tax payment, Shazeda Ahmed also claims that the Supreme People’s Court shares data with Sesame Credit on blacklisted individuals who have violated court verdicts. Sesame Credit does not, however, share data with the government, or any other third parties, without consent from the user. As can be deducted from this summary of data sources, this data infrastructure strongly resembles the political-economic infrastructure sketched in the first chapter: Sesame Credit has access to data from the Alipay platform, the Alibaba ecosystem, cooperating third parties, and government agencies. Besides the vast amount of data accumulated, it also shows Sesame Credit’s access to information on different aspects of the user’s life. As we have seen in the previous section, the five categories of data that Sesame Credit uses to compute the score are: personal characteristics, (contract) fulfilment capacity, credit history, interpersonal relations, behaviour and preference. Indeed, rather than basing the score solely on traditional financial information, Sesame Credit primarily relies on data on interactions between the user and other entities. Interactions in digital environments leave traces that can be collected and analysed. Many of these interactions can be deemed ‘social’ in the conventional meaning of the word: these include interactions between users, for example by becoming ‘friends’ in the social network section of Alipay, or interactions between users and merchants when buying products and talking about it via messaging services. However, a major part of the data is collected from

38 interactions between the user and non-human entities, such as user-interface interaction or the buying of products. In a Latourian sense of the word – i.e. a momentary association between entities (65) – this interaction between humans and non-humans can also be termed ‘social’. Sesame Credit thus bases its calculation on the datafication of social interactions, such as online friendship connections, following companies, public commenting on products, shopping behaviour, product preferences, and user-interface interaction, etc. Datafication, as defined by Annika Richterich, is the “quantification of social interactions and their transformation into digital data” (1). It thus entails a translation from a ‘real-world’ interaction into the binary language of information. When an interaction is recorded, a data point is created. Within this moment of translation, the interaction, or even a particular aspect of it, is singled out while the context of the interaction is neglected. So rather than simply ‘capturing’ a complete (social) event, datafication decontextualises a single element out of its environment. What element is recorded and to what parameters the data point is constructed wholly depend on the sensor’s capabilities and how it is programmed and attuned. Deciding what is in included and excluded in the process of datafication is a political choice made by the programmer. As such, data does not objectively ‘capture’ the interaction, but interprets and frames it. So contrary to what is commonly understood, data are not objective representations of social interactions. ‘Raw data’, Van Dijck explains, is an oxymoron (Datafication 201): rather than a raw material that can be ‘mined’ and refined, data is constructed. Even the word ‘data’ itself – from ‘datum’ in Latin: ‘that which is given’ – is misleading in this aspect as it is not a given fact, but rather a socio-technical construction (Richterich 11). In Latourian terms, ‘data’ is thus a mediator that transforms social interaction into measurable, manipulatable and monetizable quantities.

Algorithms The second actor to be considered in the network of Sesame Credit is the algorithm. As data, collected from social interactions, are algorithmically interpreted, evaluated and transposed into a numerical score, algorithms have a decisive impact on the raking of the user, and, consequently, his or her financial possibilities. It is, however, rather difficult to analyse the exact algorithms at work in the computation of the score. First of all, these

39 algorithms are important trade secrets. Sesame Credit does not grant public access to these codes to protect its market position. Secondly, most algorithms are highly complex codes – requiring specific technical knowledge – and often adapt to their environment over time through machine learning (MacKenzie). Even the original programmers of machine learning algorithms are often unaware of the inner workings of the algorithms they programmed. Besides the constant tweaking by programmers, massive amounts of real-time data input continuously mutate the algorithms of Sesame Credit. The algorithms are thus ‘blackboxed’: only the inputs and outputs are visible while the inner workings remain hidden. The application provides little specific information about what and how actions influence score, besides the general explanation of the five data categories. And, as shown in the technical walkthrough of the previous section, trying to manipulate the algorithm does not reveal which actions affect the score either. This unclarity has sparked many questions and debates, primarily in Anglophone journalistic articles. How the score is computed and how to ‘upgrade’ a score has been hot topics of discussion.13 As the algorithms remain opaque, many rumours have caught fire about what would influence the score. For example, some articles state that changing address would lower the score (S. Ahmed). In addition, vague and sometimes contradicting statements from Sesame Credit seniors have fuelled this discussion (Creemers 23). Ant Financial’s chief data scientist Yu Wujie has stated that regularly donating to charity would have a positive influence on the score but did not specify frequency that was desired. Li Yingyun, the technology director of Ant Financial, has stated that “[s]omeone who plays video games for 10 hours a day, for example, would be considered an idle person, and someone who frequently buys diapers would be considered as probably a parent, who on balance is more likely to have a sense of responsibility” (S. Ahmed). Although this was later denied by Ant Financial, many Western journalists had already incorporated it in articles on corporate social control (Creemers 23).14 In this section, I am not able – nor do I intend – to confirm or deny any of these rumours. It is simply impossible to know the inner workings of the algorithms, and not very

13 See for example: “I fixed my poor credit score by being a more loyal Alibaba consumer” (Z. Huang) 14 See for example: “Big Data Meets Big Brother as China Moves to Rate its Citizens” (Botsman); “How China Wants to Rate Its Citizens” (Fan); “China’s “Social Credit System” Will Rate How Valuable You Are as a Human” (Galeon); “China 'Social Credit': Beijing Sets Up Huge System” (Hatton).

40 productive as they are constantly changing. Rather, I give an account of what algorithms do and what this means for Sesame Credit. Algorithms are often described as a recipe: a step-by-step encoded directory that describes a sequence of actions to be undertaken that transforms a certain input into a certain output (Gillespie, The Relevance 167; Van Dijck, The Culture 30). Algorithms are automated decision-makers that govern the flows of information. Often, they are used to make predictions about the future, as is the case in Sesame Credit. As such, algorithms constitute a way of knowledge production. It is important to note, however, that although algorithmic computation looks like an objective, ‘scientific’, and neutral way of producing knowledge, the decisions it makes, and the criteria and presumptions it presupposes, are politically and ideologically laden. Bernhard Rieder notes that it is not a simple "presentation of numerical facts", but rather a “cognitive operation that generates an interpretation of the relationship between numbers and, by extension, the world they purport to describe” (105; emphasis in original). Tarleton Gillespie adds that algorithms judge the relevance of data by deciding their respective weight in the calculation. Relevance, however, is not an independent and fixed point of reference. It is rather a flexible concept which’ meaning depends on the position of the speaker. The criteria that are used to evaluate the data are also far from neutral as their boundaries depend on the entity that formulates them. As such – and not surprisingly – Alibaba’s commercial interests are inscribed in the algorithmic composition of the score. This might seem like an obvious statement since Alipay does not want to lose money granting loans to risky individuals, but this becomes problematic considering that Alibaba’s corporate interests determine the financial possibilities of individuals. Furthermore, taking into account that Sesame Credit conveys (non-financial and social) behavioural norms, these commercial – not to mention political – normative underpinnings in the calculation of credit scores may have far-reaching effects. Gillespie adds to this that aside from commercial and political interests, unconscious normative assumptions are also encoded into algorithmic decision making. John Cheney-Lippold describes the workings of algorithms as the categorisation of data: "[t]hrough algorithms, commonalities between data can be parsed and patterns within data then identified and labelled" (168). The multiple categories that describe the data reconstruct a subject’s identity. This reconstruction is what he calls a ‘new algorithmic

41 identity’ (165). These categories are, however, not fixed, but rather flexible and adaptable. When an algorithm attaches a certain label to a subject, this data is also added to the definition of the category. For example, a subject might be labelled ‘male’ according to his/her online behaviour, but at the same time, the algorithm adapts the category of maleness to this new data. Through this machine learning, algorithms can alter the original definitions of categories provided by their ‘training data’ and can incorporate factors that were not programmed originally (MacKenzie). Thus, “algorithms allow a shift to a more flexible and functional definition of the category, one that de-essentializes gender [or other categories] from its corporeal and societal forms and determinations while it also re- essentializes gender as a statistically-related, largely market research-driven category” (Cheney-Lippold 170). In the case of Sesame Credit, this means that categories like ‘riskiness’, ‘credibility’, ‘trustworthiness’, and ‘morality’ become categories defined by the parameters of Alibaba’s business model and, although a bit more remote, the government’s political aims. Furthermore, through machine learning, the definitions of the categories are based on the behavioural averages of the whole user-base. As such, algorithmic categorisation may develop discriminatory factors in the definitions of categories. For example, Sesame Credit’s algorithm may find a correlation between ‘high education’ and ‘paying bills on time’ and thus develop a bias towards higher-educated individuals. While overall higher-educated people may pay their debts more on time, this bias systematically discriminates lower- educated individuals. As such, the norms that are inscribed in the technology are both programmed by the platform owner and algorithmically constituted and modulated. The online identity of an individual becomes a conglomeration of the multiple categories identified in his/her data traces that are saturated with the behavioural patterns of the user-base. Louise Amoure describes that algorithms ‘connect the dots’ and fills gaps of information to make a complete and whole visualisation: “by connecting the dots of probabilistic associations, the algorithm becomes a means of foreseeing or anticipating a course of events yet to take place” (22). As such, the data collected from social interactions becomes recontextualised into a predictive model of future behaviour.

42 Content The content of the Sesame Credit application consists of the score – a numerical and visual representation of the user’s ‘new algorithmic identity’ – and a selection of advertisements and offers, that taps into the network of merchants that are connected to the Alibaba ecosystem. These advertisements are algorithmically suggested based on this score and user preferences and offer certain privileges or financial opportunities. There is also a section with news articles about finance and economy, but this will not be further discussed as it is of little importance in Sesame Credit’s mediation of norms. Cheney-Lippold theorises his notion of the ‘new algorithmic identity’ in the Deleuzian terminology of control; the digital identity of the user is constituted by a constant feedback loop of input data, the subsequent adaption of the identity, and updated content that matches the user’s identity. In Sesame Credit, the score is continuously updated as new user data is generated. Consequently, other content too, such as advertisements, offers, and financial opportunities, are adjusted to this identity. This, again, has an effect on user behaviour – discussed in the next chapter. The user’s algorithmic identity is thus, like the categories that constitute it, constantly modulating. In “Postscript on the Societies of Control”, Deleuze calls this form of power, which gradually replaces Foucault’s disciplinary power, ‘control’. Surveillance systems in this form adapt to the surveillance subject, rather than that the subject is disciplined according to pre- constructed societal norms. Deleuze states that “controls are a modulation, like a self- deforming cast that will continuously change from one moment to the other, or like a sieve whose mesh will transmute from point to point” (4; emphasis in original). Although his initial idea is somewhat broadly articulated, the theory further developed in surveillance studies. Kirsty Best identifies three characteristics commonly ascribed to Deleuzian surveillance:

[1] Surveillance is understood to involve the manufacturing and manipulation of simulations, rather than the circulation of transparent representations of its targets. [2] Surveillance is characterized as slippery, smooth and encompassing of everyday life, rather than organized into disciplinary sites. [3] Finally, surveillance is seen as participatory, perpetuated by the subjects of surveillance themselves. (9)

43 In this theory, surveillance subjects are seen as assemblages – a multiplicity of information, processes, materials, etc. – that can be deconstructed (Haggerty and Ericson). So rather than individuals, Deleuze calls the surveillance subjects ‘dividuals’ (5). These ‘dividuals’ are monitored by the technologies of control – i.e. computerized systems – and leave data traces that are again reassembled into a ‘new algorithmic identity’, a simulation (Best), ‘data double’ (Haggerty and Ericson 606), or ‘digital alter ego’ (Amoore 18) of the original subject. Louise Amoore argues that these are technologies of visualisation, rather than of Foucauldian ‘watching’ (22). The score is the visualisation of the user’s digital alter ego. The score is thus not a direct representation of the original user, but rather a representation of a simulation. It signifies the risk the user poses in potential future transactions, based on the calculation of data from the whole user base. The score is a dividual in the sense that it is composed of a multiplicity of fragmented recordings of behaviour from other users. As such, it does not say much about the actual (future) behaviour of the individual user, but rather about what the user means in probabilistic terms for Ant Financial. This works in the business model of Sesame Credit as the ‘miscalculations’ will be averaged out over the whole user base. Still, the score is regarded as an objective representation of the user, rather than a simulation. Following the Big Data logic that considers "possibility as already contained in actuality", the individual user is disciplined according to this simulation (Reigeluth 250). In fact, in many cases the data double, represented by the score, functions as a ‘stand-in’ of the user: it grants or denies access to offers and deals provided in the advertisements on Sesame Credit; it (dis)allows users to receive loans; and it permits users to go through faster application procedures applying for Singaporean or Schengen visa and use fast lanes at Beijing International Airport (Creemers 23). Furthermore, as the Sesame score is also incorporated in other applications, such as P2P services platform Daowei and dating platform Baihe, it accumulates meanings that extend beyond the financial realm. As such, the user’s data double constructed by Sesame Credit gains a deciding role in social interactions outside financial transactions. In these situations, it is not the actual individual that is evaluated, but rather his/her data double. To digital finance systems, merchants, and potential lovers, it is not the actual human being behind the data traces, but rather the externally composed simulation that is of foremost concern (Hier 402). In Best’s words, these simulations are “perpetual copies

44 which displace and make redundant the original – pure simulacra in Baudrillard’s sense, as the original, once displaced, ceases to matter, to have a voice, or even a reality.” (10)

Interface The interface connects the internal mechanisms controlled by the platform owner with actual usage of the user. José van Dijck distinguishes between an invisible, internal interface and a visible interface. The “internal interface links software to hardware and human users to data sources” (31). It hides the algorithms and data streams ‘under the surface’ of the visible interface. At the same time, it is the visible interface where codes and data materially manifest themselves and make meaning. Here, it is the design, the available functions and features, the tone of the content, etc. that steer the interaction with the user. “Interfaces, both internal and visible, are an area of control where the meaning of coded information gets translated into directives for specific user actions.” (Van Dijck, Culture 31) This section uses the concept of ‘affordance’ to analyse how the interface transforms the message of the coded mechanisms into a meaningful set of buttons, designs, symbols, functions, and features. James J. Gibson coined the term ‘affordance’ in ecological psychology. He defines the concept as: “The affordances of the environment are what it offers the animal, what it provides or furnishes, either for good or ill.” (Gibson 127) For him, affordance was a relational property as it “refers to both the environment and the animal” (Gibson 127). The actions that an object affords, depends on the properties of the animal too. A decade later Donald Norman adopted the concept was adopted in design studies to study how design shapes the perceived affordances of the user by encouraging or constraining certain actions (Bucher and Helmond 6). Hence, it made its way into new media research and interface analysis. Mel Stanfill, borrowing her terms from Hartson, uses three types of affordance to analyse web interfaces: functional, cognitive, and sensory affordance (1063). Functional affordances refer to what actions the interface allows for, stimulates, or restrains. As such, functional affordances are normative, they claim what users ought to do. They are not, however, determining as users might circumvent the inscribed norms (for example by hacking). Cognitive affordances have to do with meaning-making – labels, tags, descriptions, etc. – and address particular types of people. Last, sensory affordances refer to the sensory

45 – in the case of Sesame Credit only visible – aspects of the interface, such as colour, font size, page placement, etc. Overall, the interface of Sesame Credit affords and encourages the consumption on credit. Adds have a prominent place on the first page of the application (although one needs to scroll down), and the whole second menu section is devoted to advertisements that offer deals for specific credit scores. Often using vivid colours, the ads demand the attention of the user. It thus makes the normative claim that users ought to be consumers. Evidently, the promotion of credit consumption benefits the business model of Alibaba as it supports Sesame Credit-associated merchants, but it also falls in line with the goals of the government’s credit culture. The interface further constructs norms that support the business model of Alibaba. Particularly, it promotes the use of the Alibaba infrastructure and encourages the user to give up information. The five domains, marked by simplistic symbols, afford the user to understand what actions generally affect the score. ‘Behaviour and preference’, for example, explains that shopping behaviour, money spending, and transferring money influence the score. This makes the normative claim that shopping and spending money via the Alipay platform is regarded as good. Similarly, the description of ‘fulfilment capacity’ encourages people to use Alipay as the payment management system for billing, for example of property, which allows Alipay to collect data from these transactions. ‘Interpersonal relations’ implies that connecting with other individuals (with a high score) via the Alipay platform has a positive effect on the score. ‘Personal characteristics’ promotes to give up personal information, further visually encouraged with coloured stars. In his forthcoming paper, Hao Wang argues that disciplinary power in social credit systems, including Sesame, credit works through these indications for preferred behaviour. As the algorithmic computation is blackboxed, users do not know exactly what actions have a positive or negative influence on the score. The explanations of the data categories function to communicate these behavioural norms instead. Although the exact workings of the algorithm remain opaque, they provide what Wang calls a ‘first order transparency’ which ‘disclosing the general working principles of algorithms.’ (15) The score is itself a cognitive affordance that allows the user to understand his/her financial (and non-financial) possibilities. By framing it as a personal score, placing it central in the app, the interface affords a personal identification with the risk calculation. It also

46 presents the score as a neutral and scientific calculation of the user’s personal creditworthiness. The score, as a number, already implies that ‘trustworthiness’ is measurable. This statement is visually supported by the placement of the score in the middle of a gauge chart – a universal symbol for (scientific) measurement (Image 8). It connotates a speedometer, whose level can easily be influenced by ‘giving more gas’, or in the sense of Sesame Credit, by behaving in the desired way. At the same time, the meter’s measurement points are visualised by sesame seeds, and the background depicts a natural environment with mountains and trees. The nature-connoting colour scheme of blue and green also supports Sesame Credit’s natural look. This reinforces the score’s neutral and impartial appearance. The page that explains the five domains also invokes scientific connotations; the background shows a pentagon15 that – literally – connects the dots of the five categories, implying that this data can indeed create a complete picture of the user’s profile (Image 9). Sesame Credit’s ‘scientific’ and ‘natural’ design implies that the score is objective and reinforces dataism’s dogma of objective quantification. Most importantly, many functional affordances of the application depend on the score. The user’s score ultimately gives or denies access to loans, offers, etc. It thus makes a normative claim about what users ‘deserve’ certain privileges and financial opportunities. The advertisements also show the desired scores, making the normative claim that these offers are only for a particular kind of people: namely high-score individuals. Sesame Credit’s interface thus makes normative claims about the desired behaviour and properties of users and actively shapes user interaction by restricting, guiding and encouraging certain actions. In this way, the application also affords Alibaba to manage its user base. It is important to note, however, that the interface, and its underlying mechanics, do not determine user interaction. Nagy and Neff stress the importance of user perception in the affordances of a technology. It is the understanding, belief, and expectations of the user that determine the ‘imagined affordances.’ This will be the central topic in the next chapter.

15 In earlier versions of Sesame Credit, this pentagon was actually on the foreground visualising the relative evaluation per category.

47

2.4 Reassembling social credit As we have seen, the financial platform Sesame Credit consists of a network of technological actors that mediate the interaction between Alibaba, merchants and users. In this process, the technological actors transform, translate, and modify these (inter)actions. Through practices of datafication, data translates human behaviour into the binary language of computer systems, thereby decontextualising the original event. Algorithms compare and categorise these data and reassemble them into simulations of the user that are used to predict future risk. The score signifies this data double but is represented as an accurate and ‘scientific’ visualisation, which ultimately decides the privileges and financial opportunities of the user, which are granted by Ant Financial and supplied by third-party merchants. The technological actors are thus mediators, as they “transform, translate, distort, and modify” user behaviour into a meaningful future risk prediction (Latour 39). In extension, Sesame

48 Credit, as a technological platform, also is a mediator that transforms the interaction between users, merchants, and Alibaba. Following Best’s first characteristic of Deleuzian surveillance, it is through control and modulation of digital simulations that Sesame Credit mediates power relations between Alibaba and the users. For Cheney-Lippold, algorithmic categorisation is a biopolitical practice that divides the population into multiple manageable normative segments (173). As the constant flood of input data continuously modulates these categories, the norms they produce are not static either but somewhat ‘elastic’. As such, Cheney-Lippold suggests a softer version of biopolitics that includes besides discursive construction also algorithmic modulation of these categories and norms (175). Indeed, Sesame Credit regulates its user base through the categorisation of data and the construction of algorithmic identities. These algorithmic identities then give or deny access to certain features, disciplining the individual user. As the score provides feedback on the user’s behaviour, Sesame Credit is a technology for self-surveillance and self-governance. Reigeluth explains that this “‘algorithmic governmentality’ relies on the dream that reality, if correctly probed and recorded, will reveal its own passive, inoffensive and non-coercive normativity […], to which the individuals need only adapt as painlessly and seamlessly as possible.” (Reigeluth 251) Indeed, the Sesame score does not simply ‘reflect’ one’s personality, but rather is a normative visualisation to which the user has to adapt. This way, the individual ‘self’ becomes modelled after the digital – dividual – ‘self’. It is the platform owners that have access to the resources of data and construct these data doubles. Through practices of data warehousing, i.e. the collection and storage of data, data mining, finding correlations and patterns in Big Data through algorithmic computation, and profiling, constructing digital simulations of individual users, Alibaba, backed by the Chinese government, controls the parameters of users’ digital identities (Reigeluth 246). As such, a market-logic is inscribed in Sesame Credit that reconfigures norms that steer users into profitable behaviour. Users have little control over what their online doubleganger is composed of. In Cheney-Lippold’s words: “[w]e are effectively losing control in defining who we are online, or more specifically we are losing ownership over the meaning of the categories that constitute our identities” (178). In short, Alibaba uses

49 modulating data doubles as technologies of governance and self-governance that create commercially and politically-driven norms. As a platform, Sesame Credit reconfigures norms of social interaction: between users and financial institutions; between users and merchants; and between users themselves. It sets new norms for how and to whom consumer loans are granted; through Big Data analytics, Sesame Credit is able to calculate credit scores for consumers and small businesses that would be traditionally ‘unscorable’ due to lack of financial information (Packin and Lev-Aretz 355). This might indeed lead to broader ‘financial inclusion’ and economic growth – as is much desired by the Chinese government – but poses different problems like algorithmic discrimination discussed above. Sesame Credit also reconfigures norms of ‘trust’ in market interactions: trust is equalled to a calculation of risk, which is based on a simulation of user behaviour. Indeed, also in human to human interactions, Sesame Credit’s score changes the norms of sociality: scores might determine the online social ties between users and may even play a role in love affairs. These reconfigurations of social norms, either for good or for ill, are driven by commercial and political intentions. As these simulations are based on the social interactions of users and norms are reconfigured with market-driven intentions, sociality becomes increasingly more entangled with commerciality. It is thus by socialising credit – calculating credit scores from social interaction – that social credit monetises sociality. Besides social norms, Sesame Credit also reconfigures commercial, moral, and political norms (etc.) and inscribes a normative way of usage of the credit system. Datafication is key in this process as it translates social interaction into computable, and therefore measurable, units. As social interaction is translated into points of data, Sesame Credit is able to “measure, manipulate, and monetize online human behaviour" (Van Dijck, Datafication 200; emphasis removed). Through the process of datafication, multiple areas of life previously untouched by market rationality now also gain monetary value. Sesame Credit inserts a “financial logic into everyday conduct” as everyday actions of users are measured and (e)valuated (Van Doorn 358). Niels van Doorn describes this “‘real subsumption’ of life by capital” of commensurating machines, such as Sesame Credit (357). Though datafication and quantification of everyday activities, these activities are transformed into financially calculable ‘investments’ in the user’s human capital, “a machinic assemblage of the worker

50 and her skills and capacities” (Van Doorn 357), that determines his/her market position in the reputation economy. Furthermore, Sesame Credit establishes a space for competition. As the score can be implemented on multiple other platforms, including dating sites and P2P services platforms, Sesame Credit creates a common ground on which reputations may be compared. Relatively higher reputations may lead to more financial and social success (although this social success is based on a financial risk prediction). By offering feedback-based monetary rewards on the user’s behaviour and creating a ground for competition, Alibaba provides a gamifying element in the everyday lives of users that fosters commercial norms and participation. Julie E. Cohen describes gamification as “the application of concepts and techniques derived from games to foster styles of ‘engagement’ that promote business objectives in other areas of activity” (208). This creates a certain governmentality where the subject does not passively follow the rules but is actively engaging with the environment in search of personal benefits. In this way, the rational economic behaviour of users that pursue their own monetary goals becomes part of Alibaba’s game-design. As Cohen puts it: “We are playing, but we are also being played.” (208) The user thus becomes an “entrepreneur of himself”, or ‘homo economicus’, in Foucault’s terminology (Birth 226); a rational agent that is actively looking for benefits and opportunities of investment his/her human capital in competition with other users. By imposing gamifying elements in everyday life and stimulating a competitive governmentality, Sesame Credit reconfigures social, moral, political (etc.) norms in the everyday lives of users in favour of Alibaba’s business model. The next chapter analyses the user as a rational (economic) agent that interacts with Sesame Credit as a commensurating machine. While this chapter focused on the norms that are inscribed in Sesame Credit as a technological platform, Chapter 3 analyses its ‘explicit use’: how actual users interact with the credit platform (Van Dijck, Culture 33).

51 Chapter 3: Explicit Users of Sesame Credit

3.1 Introduction While the previous chapter focused on the interaction between non-human actors, this chapter centres around human actors engaging with Sesame Credit: the users. It addresses the question how Sesame Credit is used explicitly in everyday life. How do users engage with the technological actors and content described above? And how are Alibaba’s norms incorporated in the everyday lives of users? The research question of this chapter is: “How does disciplinary power operate through the everyday user practices of Sesame Credit and what are the user’s motivations for use?” To answer this question, this chapter approaches Sesame Credit users and their usage ethnographically. Van Dijck suggests that “explicit users figure as ethnographic subjects: their usage of social media may be observed and analyzed in situ; they may also be interviewed about their habits or practical use.” (Van Dijck, Culture 33). Research on this user-perspective on social credit systems is still scarce. One recent study by Genia Kotska, however, maps the attitudes of Chinese users of commercial and government-run pilots of the national Social Credit System based on her quantitative research. She concludes that many Chinese citizens approve of commercial social credit systems, such as Sesame Credit, and government-run pilots of the national SCS. Although levels of approval were relatively high in general, Kotska concludes that “strong supporters of SCSs are more likely to be older, have a higher income, male, more highly-educated, and live in urban areas” (21). Although wealthier and higher-educated individuals are expected to be more critical of surveillance-increasing and freedom-limiting social credit systems, she explains that their high levels of approval might be explained as citizens with these demographic characteristics are the primary beneficiaries. A second explanation she provides is that social credit systems might also be perceived as “an instrument to improve the quality of life and to close institutional and regulatory gaps, leading to more honest and law-abiding behavior in society” (21), instead of privacy-limiting systems for social control. Contrary to Kotska’s quantitative approach, audience studies provides a theoretical starting point for a qualitative study on the users of Sesame Credit. Originating from the Birmingham Centre for Contemporary Cultural Studies, audience studies takes a critical

52 orientation towards empirical audience research. This critical orientation, according to Ien Ang, adopts a self-reflexive stance (Politics 175) and takes into account the cultural processes that construct the audience:

what is at stake is not the understanding of ‘audience activity’ as such as an isolated and isolatable object of research, but the embeddedness of ‘audience activity’ in a complex network of ongoing cultural practices and relationships. (Ang, Politics 180)

Following this critical orientation of audience studies, this chapter does not aim to ‘map out’ the practices and opinions of Sesame Credit users but to connect them to larger discourses, power-structures, and ideologies. Audience studies presupposes an ‘active user’, meaning that it recognises the agency of the user in the process of meaning-making. David Morley argues for an "active production of meanings by viewers" (Ang, Politics 178), in his analysis of the audience of the television program Nationwide. Going against a textual determinist position that presupposes that media audiences always interpret the meaning ‘intended’ by the producer, his work makes clear that use and consumption are sites of struggle for the production of meaning. Stuart Hall theorises this in his famous essay “Encoding/Decoding”. Hall, too, takes the case study of television, in which he argues that producers encode a message in the programmes they produce. The programme acts as a ‘sign-vehicle’, which allows the message to circulate. The audience, then, decodes the message by interpreting it. Hall explains that the audience decodes the message in a variety of ways, often differing from the intended message encoded by the producer. He distinguishes three clusters of audience reception: a dominant-hegemonic position, in which the audience decodes the message within the preferred readings of the producer; a negotiated position, which “contains a mixture of adaptive and oppositional elements” (Hall 172); and an oppositional position, in which the message is decoded “in a globally contrary way” (Hall 172). Although audience studies mainly focuses on the process of meaning making, it neglects the material practices of users, i.e. the physical interaction with the medium. Refuting a behaviourist perspective, Hall argues for the importance of meaning in material effects: “[i]t is this set of decoded meanings which ‘have an effect’, influence, entertain,

53 instruct or persuade, with very complex perceptual, cognitive, emotional, ideological or behavioural consequences.” (Hall 165) Practices, according to Hall, follow the moment of decoding. Indeed, how users engage with Sesame Credit depends on how they make sense of the platform. I like to stress, however, that material practices and processes of meaning-making are entwined. As Barad and other post-humanists remind us, materiality and discourse are inextricably entangled. The ways in which users use Sesame Credit are linked to how they think about the system and vice versa. Just as “the moment of decoding should be considered as a relatively autonomous process in which a constant struggle over the meaning of the text is fought out”, the moment of consumption also figures as the site of struggle between implicit and explicit use (Ang, Politics 175). Moreover, as we have seen in the previous chapter, Sesame Credit uses discursive means (e.g. the interface, content) and material constraints (e.g. not being able to borrow a bike or lend money) to encourage its preferred reading and use. Therefore, in this chapter analyses both the material practices and the processes of meaning-making. To study the everyday practices of Sesame Credit users and their motivations for use, I performed six in-depth semi-structured interviews (Bernard 212) with Chinese people within my social circle during my research trip to Shanghai. The respondents are aged between 23 and 27 and all come from Shanghai or other urban areas in China. The ratio of male/female respondents is 50/50. The interviews took place in an informal setting, at the respondent’s home, in a café, or over the phone, and were structured like informal conversations. The interviews were held in English, as my Chinese is insufficient for full conversations. This posed some difficulties as some participants had problems expressing themselves in English or could sometimes not understand the questions fully.16 The names of the informants are replaced with common given names in China to ensure their anonymity. It must be noted that, as the scope of this research is limited, it functions as a pilot research in qualitative audience research on users and usage of social credit. During the interview, I asked the participants to walk me through their Alipay application, following the guidelines of Light, Burgess and Duguay’s ‘walkthrough method’,

16 I left language mistakes in the quotes so that they reflect the answers of the interviewees as much as possible. In some cases, however, when their answers would risk being incomprehensible, I added or changed words (in square brackets) according to what I think the informant meant.

54 to show me how they would normally use Sesame Credit and what functions and features were important for them. This enabled me to focus on the everyday practices of users and their interaction with non-human actors in Sesame Credit, besides their motivations for use. This chapter uses Wetherell and Potter’s methodology of discourse analysis to analyse the interviews and identify interpretive repertoires. They argue that discourse has multiple functions that are action-oriented and performative (169); people say things with a particular goal in mind, but at the same time repeat from larger discursive systems, often without being aware of it. Functions can be interpersonal, such as excusing or blaming, but can also have broader purposes. Discourses vary according to their function. As such, they note, variation is thus an "analytic clue" of a discourse’s function (171). Thirdly, discourse is constructed “out of pre-existing linguistic resources with properties of their own” (171). Individual speakers thus make an active selection of the resources they use to construct their account. Again, variation indicates the multiple ways in which these accounts are composed. Interpretive repertoires, then, describe the regularities in variation; although an account of an individual speaker can vary greatly and can even be in contradiction with itself, many people may use the same repertoires to construct their accounts. In this chapter, I have identified common discursive repertoires surrounding social credit and, particularly in the last section, examined the functions of these repertoires. Section 3.3 discusses the everyday practices of users, focusing on their interactions with non-human actors in Sesame Credit (data, algorithms, interface, and content) and the ways in which disciplinary power operate through these interactions. Following José van Dijck’s model, this section can be taken together with Chapter 2 as an analysis of the network of technology, content, and user/usage that complements the political economy- view of the first chapter (Culture 28). Hereafter, I focus on the audience reception, using Wetherell and Potter’s methodology of discourse analysis. Before getting to this, however, I will first reflect on my position as a researcher in my research project.

3.2 Self-reflection The self-reflexive perspective of audience studies “is, first, conscious of the social and discursive nature of any research practice, and, second, takes seriously the Foucauldian reminder that the production of knowledge is always bound up in a network of power

55 relations” (Ang, Politics 175). In this section I reflect on my research and my position as a researcher, starting with an autobiographical account on how I started my research on Sesame Credit. About one year ago, a classmate who was aware of my interest for Chinese society (I already visited China earlier for three months) sent me the Wired article “Big data meets Big Brother as China moves to rate its citizens” by Rachel Botsman. The article conveys an Orwellian vision about how the Chinese state would watch and rate its citizens by 2020 in cooperation with tech giants such as Tencent and Alibaba. Other articles I read confirmed this view and added comparisons to ‘that one’ Black Mirror episode (i.e. ‘Nosedive’). Admittedly, I immediately imagined how this would be a good topic for my thesis in the light of a dystopian version of Deleuze’s ‘control society’. Reading into the scarce academic literature on Chinese social credit, I quickly learned that ‘citizen scores’ were less definitive than the Anglophone media suggested. Moreover, the relation between Alibaba and the Chinese government is ambivalent and cooperation is challenging as both parties have their own interests. I realised that my first reaction was fuelled by a particular Western discourse which opposes China to ‘the West’. Ien Ang points out that “‘China’ has often been useful for Western thinkers as a symbol, negative or positive, for that which the West was not” (Ang, Chinese 32). The dystopification of Sesame Credit falls in the tradition of “intense and extreme dramatization of events” in Western media (Ang, Chinese 32). In short, discourses around Sesame Credit frame China as the West’s great ‘Other’. Throughout this research, however, I discovered that Sesame Credit as a consumer credit service has many Western equivalents – although often smaller in scope – and that reputation mechanisms are inseparable from new media platforms. By taking up the perspective of the Chinese user, this audience research aims to move away from a Eurocentric view on Sesame Credit, and instead take China as a point of reference (Chen). As explained in the introduction of this thesis, I made a research trip to Shanghai, where I used Alipay and Sesame Credit on a daily basis and spoke to several Chinese users of the credit system. In the first few months, I experienced a culture shock due to different cultural and societal standards. One of these differences is the increased personal restrictions for the ‘good of society’ – such as the many security checks at metro and train stations, and restrictions on internet use by the ‘Great Firewall of China’ – and the indifferent attitude of many Chinese towards this. I was also amazed by the magnitude of

56 digital platforms, such as Taobao, Alipay and WeChat, and their significance in everyday life in urban China. The culture shock, however, also gave me a fresh look on my own cultural standards. I aim to show this reflexive aspect in this audience study. Although the object of research is the Chinese user, Sesame Credit must be seen in the context of a global platform society and growing reputation economies. As such, this research can also teach us about everyday practices and relations with new media platforms in Western societies or in other places of the world. As Kuan-Hsing Chen notes, “to do area studies is not simply to study the object of analysis but also to perform a self-analysis through a process of constant inter-referencing.” (253) As a beginning Sesame Credit user (with a very low score!), I had a unique position to ask about the informants’ user practices. Participants would explain the tips and tricks of using Sesame Credit by telling how they use the platform. As a (want to be) Sesame Credit user myself, I was thus not only observing and learning about their practices, but also participating in the cultural practice of using the credit system and learning from Chinese users themselves. This opens up a complex discussion of power relations between the researcher and the informants. Sara Ahmed reflects on this double position of the ethnographer as participant-observer: “this double vision [of the ethnographer] involves seeing the informants as both friends and strangers” (S. Ahmed 72). Indeed, even though all participants were friends of mine (I had met them prior to their involvement in the research project, outside the ethnographic context), a distance remained between the informants and me due to the (institutional) academic reason behind our conversations. As Clifford Geertz reminds us, any ethnography involves a series of interpretations and is, therefore, a construction of the ethnographer (241). Though I do not aim to ‘speak for’ any of the Sesame Credit users, it must be noted that this audience study is my personal reading of the informant’s responses. The interviews are, however, taken as a guideline through which my interpretations are constructed.

57 3.3 Everyday user practices This section focuses on the everyday practices of users: how they interact with non-human actors in Sesame Credit – data, algorithms, and interface – and how disciplinary power operates through these everyday interactions.

Registration and entry Most of the users reported that they were introduced to Sesame Credit after an update of the Alipay application, since they had been using the app already before de launch of the credit scoring system. Sesame Credit appeared as an extra function in an already familiar payment environment. For many users, the introduction of Sesame Credit happened unconsciously, and the credit system was subtly integrated into their everyday consumer habits. Yan, a 21-year old girl, illustrates this:

So, in the beginning Alipay [was] just doing like this [mobile payment]. Nothing else from the beginning. But just after a few years they have something more and more. And suddenly I even didn’t notice yet and I have, like, Sesame Credit. I don’t know how [it is] even possible I have this.

The registration process, however, requires the user to actively sign up for the service and provide scans of one’s ID card, as is shown in the previous chapter. Of course, it is likely that users do not remember their registration as for most of them it is over three years ago. Still, some participants expressed that they do not feel it was their active decision to commence using Sesame Credit, but rather something that came naturally with an upgrade of the app. Ming, a designer in his twenties, tells:

I didn’t start it because we, you know I… we have to upgrade the app. And if we upgrade the app, new function[s] will be added. And, you know, Zhima Credit, it’s just one part of the Alipay’s company. So maybe it’s the company’s business brand,

58 so we just use it. And I […]17 forgot, maybe I have signed some contract, confirmed some contract, I […] forgot.

The passages cited above show that Sesame Credit has been introduced to the users naturally, so that most did not even notice or remember how they started using the credit scoring system. As such, Sesame Credit gradually became integrated into their everyday lives.

Direct and indirect use The borrowing goods and deposit wavering functions within Sesame Credit are important aspects of the daily interactions with the system. The participants explained that they often use the sharing bike and sometimes the car rental functions of Sesame Credit and borrow umbrellas and power banks for mobile phones when they need it. One participant, Xiu Ying, a saleswoman at an LED-screen company, described that she was actively looking for these benefits within the advertisement section:

In the app, I mainly pay attention to the priorities I can have with my score. So, for example, […] the first time I use Sesame Credit was because I wanted to rent a car. And not everyone can do it, on Sesame Credit. Cause they need a score that is over seven hundred-something, I don’t remember. And I have, I have it over that. So, I want to know with, within the score I have what kind of priorities I can have, what kind of rights I can get. I usually take a look at that. And also, if they show advertisement or promotion I will click, take a look.

As this passage shows, users actively harness the benefits of the credit system in their everyday life; they use the opportunities provided by Sesame Credit whenever they need it. At the same time, however, they conform to the normative behavioural patterns inscribed by Alibaba.

17 Participant used the word “almost” in place of the two ellipses in this sentence. However, considering the context and the fact that the interview was not conducted in his mother tongue, he probably did not mean to say this word.

59 Many of the interviewees, however, noted that most of their interaction with Sesame Credit was indirect, often through other functions within the Alipay application. Especially Ant Credit Pay (Huabei) and Ant Cash Now (Jiebei) were commonly used among the participants. Ant Credit Pay is a credit card-like service that lets the user pay in advance, while the indebted amount is collected at a later stage with interest (typically at the end of the month). Ant Cash Now is a service that provides small loans. The maximum amount of money that can be borrowed in both functions depends on the Sesame Credit score and data generated by both Ant Credit Pay and Ant Cash Now are directly incorporated in Sesame Credit’s ‘Credit History’ category. These functions offer financial opportunities to users that traditionally would lack credit records, increasing financial inclusion. For example, as a student, Yan would normally have difficulty applying for a credit card. She expressed, however, that she could use Ant Credit Pay when she is in need of external financial resources:

I’m a student, I’m in the university, sometimes short of money. And suddenly they just told us like ‘oh there is something called Huabei and you can use the money you doesn’t really have right now.’ […] So I don’t have money anymore, but I still can buy stuff or pay for the restaurant or something because I use Huabei. (Yan)

She also noted that she sometimes uses Ant Cash Now as “emergency money” when she was unable to pay the rent in time. Another participant too, used Ant Cash Now to pay for his rent:

I use the Ant Cash [Now] just this year about July because I had to pay my rent […] and I don’t have enough money so I […] just make a loan from Ant Cash, and about 600… 6000 Yuan [€750,-]. And as the contract says I have to return the interest and the money in three months and I have paid them in average each month to pay around 240… 40 to 50 Yuan [€30,-], I don’t know, just including the interest. And I have returned, I have returned the money now. (Ming)

The same participant explained that he uses Ant Credit Pay a lot in his daily life as the credit payment option is incorporated in Alipay’s general payment system. He demonstrated that

60 when paying with Alipay, the user can choose if the payment is made from the Alipay balance, directly from a connected bank account, or from Ant Credit Pay. As such, he used Ant Credit Pay very often in on and offline situations:

Ant Credit Pay we almost use it every day […] and we can use it for almost everything. If we buy train ticket, if we buy any type of ticket, we can use it by Credit Pay. And if we shop online, such as Taobao. Or we order meals by app, we can use Ant Pay, Ant Credit Pay. And so we almost use it every day. This is online. If you are not online, just in your life, you can use the pay code and you can choose the way to pay. And eh… just like if you are shopping something, they will scan the code and pay the money in this way or choose any way. This is balance, and this is your card, bank card. Just like this. (Ming)

While Ant Credit Pay and Ant Cash Now are frequently used in the everyday lives of users, Sesame Credit here remains more on the background. The credit system functions as the ‘engine’ that powers these other functions by providing a personal risk analysis for these credit lending services. At the same time, data from these functions, such as the information about paying back the loan on time, are fed back to Sesame Credit and constitute the score. Similar to Ant Credit Pay and Ant Cash now, many other functions in Alipay are also connected to Sesame Credit. Yan explains that

you can buy the movie tickets, you can even rent an apartment without paying deposit, or like… less deposit or something. You can do a lot of things. But its according to your Mayi [‘Ant’, meaning Sesame] Credit grade.

Later, she mentions that “all the function[s] here [in Alipay] are actually connected with Zhima”, even medical and city services. As such, data sharing between these different functions, and the institutions behind them, has become a normalised and accepted practice in the everyday lives of users. At the same time, the fact that Sesame Credit influences the inner workings of other functions has also become normalised. Thus, while most of the interviewees reported not to interact with the Sesame Credit interface very often, the credit scoring system is, often unconsciously, at the backdrop of many everyday

61 activities. Jing, a 24-year-old girl, explains how Sesame Credit for her is only present in the background:

I use Zhifubao [Alipay], but Zhima Credit is not the thing I would usually check. It’s something like naturally what’s going on, like, back in this app.

The process of accepting more commercial surveillance and data sharing practices between companies goes hand-in-hand with Sesame Credit’s embeddedness in everyday life. As these practices happen on a daily basis, they easily become ‘normal’ for the user.

Discipline Contrary to this, another informant, Xiu Ying, explains that she is quite aware of her Sesame Credit score when she is shopping online. She attunes her consumer behaviour to what she thinks will increase her score:

I heard from others that if you buy things separately, so with the amount of money you spend, you can get more… eh the credit score. Since when you buy things all together it is just one time. And you can do it very often. Like every day you have one box arriving. That’s ok. And another thing is […] you always need to make a comment to the things you bought. Don’t just buy them and leave them there. Make a comment very seriously. Tell the truth: what do you think of the product, how do you like them and what they can improve.

As such, she tries to ‘play’ the algorithm of Sesame Credit to her advantage, even though the exact workings of algorithmic computation are unclear. Although she might indeed benefit from manipulating the algorithm, she is also conforming to the normative behavioural patterns prescribed by Alibaba: increased consumption and participation on the platform. While still rationally perusing her own goals, she also participates in modes of self- governance inscribed in Sesame Credit, taking on a certain form of ‘governmentality’. In line with this, this passage also shows that Sesame Credit’s disciplinary power works through what actions users believe to have an impact on their score. Because the algorithms are blackboxed, users don’t know exactly what affects their score: “Honestly, we

62 don’t really know how the points [are] calculated, like in detail.” (Qiang) Disciplinary power may, therefore, operate through what Hao Wang calls "first order transparency" (15): although the exact calculations are blackboxed, the general working principles of algorithmic computation are disclosed. Remarkably, none of the participants mentioned the descriptions of the five categories of data when asking about what data constitutes their score, and in some cases, I had to make them aware about it. However, all of them had an idea about what generally in- or decreased the score. Qiang explains:

One aspect is how often do you spend money. The monthly cashflow of the account. That’s very important. The second point is how often do you violate the rules. […] I would say, the rules like the borrowing, and you know like this kind of rules, like borrowing stuff. […] If you don’t return things in time, your points will lose.

Knowing this, he acknowledges that Sesame Credit “forces” himself to “follow the rule, to obey the rule”:

Like when you borrow a bike, […] if you don’t return [it] to a certain area, your credit will decrease. […] So sometimes […] if you’re done, finished using the bike, but you’re not in a certain area, you have to put the bike back to where it belongs. So like yea, I had like that experience as well.

This is a clear example self-discipline: Qiang watches his own behaviour to keep his score high. Still, Qiang states that he does not really "care" about his score and it is just "like a number" to him, at least as long as he watches himself not to "break the rule." This passage also shows that (self-)discipline is not only limited to consumer or financial behaviour but also include moral values (which, of course, also have monetary value for Alibaba), such as returning the bike properly or not damaging borrowed goods. Qiang also expresses that he would not judge people on their score: “when you make friends you don’t care about their credit”. In line with this, none of the participants felt like personal relations with their friends changed because of Sesame Credit or reported to have altered their social network because of this. By doing so, users negate Alipay’s imperative to financialize the social, albeit because they simply do not care.

63 Although most informants noted that they had little problem in keeping their score high, one explained that while he used to use Sesame Credit quite often, his score had recently dropped. Wei, a 23-year-old who just graduated and is working for his dad’s company for a few months, tells:

Yea I have a little problem. So the police catch me, throw me in the detention centre, or prison, about five months. Before I went to prison, I just do some shopping, because I have no cash left in my card. So I just use the Zhima Credit to shopping about ten quai, ten Yuan [€1,25]. But then, next day, I went to prison. So I can’t give the money back. So when I come out from prison, so I find out the score is super low. (Wei)

He later noted that he did not believe Sesame Credit had the information that he went to prison, although this is certainly a possibility. Because his score dropped below 600, he is unable to use functions like Ant Credit Pay, Ant Cash Now and any of the borrowing functions. As a just-graduated student, he tells that these restrictions have a big impact on what he can afford to buy. He explains that he used to buy expensive purchases with Ant Cash Now, so he could pay the bill back in several months. Without access to Ant Cash Now, he is unable to afford such expensive purchases:

I [was] just about to buy a cell phone, a cell phone from the Taobao. Just also belong to the Alipay company. So about the 6000 [Yuan; €750,-], so I can separate my bill to about six months. So I can only pay 1000 [€125,-] every month, so I can just afford it. But now my score is very low, I can’t separate my bill. I can’t borrow money from the Alipay company. So I just, just I can only pay the full price. So for me, I’m not have… I don’t have so many money to just pay the bill [at] once, for full price. So that’s a real problem.

He tried to repair his score, but with little success:

I called the Alipay company, make some phone calls, just… they just don’t understand. So they won’t give my score back. Just leave me to the low score.

64

Maybe they will grow back the scores, but I think it’s really slow. It’s about… one score for… per month. So I should wait years to grow it back. Or 3 or 5 years so I can just borrow something, borrow the money, pay the bills, for separate bills. So I can’t use it for years.

In short, Sesame Credit has become normalised in the everyday lives of users. To them, it is “not anything mysterious or anything new” (Xiu Ying). As Sesame Credit works on the background of other functions within Alipay, everyday activities are, consciously or unconsciously, influenced by the score. While users gain (financial) benefits from the system and sometimes actively look for them, they also take on modes of self-governance and self- discipline that support Alibaba’s business model. Ultimately, this results in a governmentality wherein users pursue their own goals, but at the same comply to the normative behaviour inscribed in the system. Wei’s story shows the danger of this situation: when one’s score drops, it can have severe effects on his/her financial possibilities and repairing a score can be a near-impossible task.

3.4 Motivations for usage This section examines the motivations for usage that the participants reported in the interviews, following the methodological guidelines of Wetherell and Potter. The first subsection discusses the informant’s concerns about Sesame Credit, which primarily centred around the topics of privacy and surveillance. The next three subsections identify three discursive repertoires that the participants used to motivate their use: a discourse of convenience, of societal benefits, and of inevitability.

Privacy and surveillance Though all interviewees were active users of Alipay and were directly or indirectly using Sesame Credit and generally had a positive attitude towards social credit systems, most of them expressed concerns about privacy and surveillance. Xiu Ying states that “there are some negative sides [to Sesame Credit]. For example, I think they collect a lot of private data.”

65 Interestingly, half of the informants connected privacy issues to the regular advertising phone calls they received. Xiu Ying explains this most clearly:

I think in China now a lot of people sell and buy private datas. So, for example, every day I receive a lot of phone calls from like real estate companies. They ask me if you need to buy apartment. And if I need this kind of service, that kind of service… but I never told them my phone number. So, I think it has a risk if I use Sesame Credit because they collected my information. […] They really bother us a lot.

She adds to this that she is worried that companies like Alipay “sell” her data “to the people who wants to make business”. Wei also states his concern that Alibaba’s data is not securely stored and can easily be hacked and sold to other companies:

If there is some bad guys to hack your service, they can get so many data from everyone. So that’s a really dangerous thing. So, the bad guys can click the every information about every people. The company is not as the government, they are so easy to hack.

Compared to private companies, he shows more trust in secure data storage of the government, because, as he states “the hacker won’t hack the government service. They just hack the company.” Although issues of privacy and surveillance were often coupled to companies that “just want to make business”, the government is generally seen as more reliable. Xiu Ying states: “Yes I trust the government. I don’t think they will use the data to do bad things to the citizens. This trust is not 100%, but at least 70 to 80%.” Qiang also stated that he was positive about a possible cooperation between Alibaba and the Chinese government for the construction of the national Social Credit System:

I believe that in the future, I would say in 10 years it will be very useful. Because I trust the vision of Jack Ma, or Alibaba. So, I do believe that once they cooperate with the government, the government will take it very… will see the value of it.

66 Contrary to this, Wei mentions that he is afraid Alibaba would sell his personal information to the government:

There is another issue, because I used Alipay to pay in the restaurants, or the entrance fee in the club, or somewhere else, the government will know your… where are you. About every day, they can maybe even know your job or something, maybe which place you often go. So that’s a problem. (Wei)

Corresponding to Kotska’s research, most participants also approved of a national Social Credit System. Ming states that he thinks “it’s very good if the government want to set the social credit system of its citizens." One participant, however, was highly sceptical on a government-run social credit system. Wei, who has a low credit score due to his contact with Chinese authorities and probably has more reason to be against increasing state surveillance. Upon asking his opinion about the national SCS, he answers:

I think that’s not a good idea. Because in China the government wants to know everything about each person. About his behaviour or something. So, they will collect so many data. Just you can see, the camera system [is] all around China, in every corner. So, you can check the… I don’t know. They can just see every movement about every people. That’s I think some time just people want to private… private things. But the government check everyone. […] their eyes. So, they don’t have the […] privacy. (Wei)

Although all respondents expressed their concerns about privacy issues and surveillance, at least in a commercial context, they were happy to use Sesame Credit. And corresponding to Kotska’s research, they also showed a high approval of a national Social Credit System. Even Wei, the most sceptical of the six respondents, also still continued to use Alipay in his daily life and had not decided to quit Sesame Credit. The question thus remains why they show such high levels of approval. The following paragraphs identify three discourses that support social credit systems, despite the concerns about privacy and surveillance that come with them.

67 Discourse of convenience The most reported reason for using Sesame Credit is ‘convenience’. All of the respondents mentioned this at least once throughout the interviews. The credit system is viewed as convenient because users gain many immediate rewards in their daily lives. The following passage shows how Qiang arrives at the idea of ‘convenience’ after summing up the possible benefits of Sesame Credit that make life easier:

If you have a history of, you know, paying money on time paying bill on time, do everything according to rule, according to the law, your life [will be] easier. […] you may not need to, you know, waste a lot of time in lining or you can have a […] on a lot of things, or you don’t need a visa to another country. Or you… you might be able to borrow things without paying deposit. […] So, you would have an easy life. I would say more convenient. That’s why… it’s actually more ‘convenient’, [that] is the word that Alipay trying to tell people, it’s like ‘make your life easier’.

Xiu Ying, one of the more active users among the interviewees, describes Sesame Credit as a ‘tool’ that improves the quality of life: “I think it’s a tool. It can really make my life more convenient: do things faster, organize things easier. And spend less money in fact.” Also Wei, who is unable to use any of the Sesame Credit-connected functions due to his low score, frames his misfortune in terms of convenience when he states: “it’s so inconvenient about my life.” And even though he expressed the most concerns about privacy and surveillance among the participants, he admitted that chose the convenience of mobile payment above using cash: “because it’s the most convenient way to pay with Alipay or WeChat”. The discourse of convenience functions to counterbalance negative aspects of the credit system. Often, convenience is weighted against privacy concerns and increased surveillance, and is seen as a worthwhile trade-off: Xiu Ying states that “for most of Chinese convenience is much more important than privacy. […] If they know my location, they know what kind of products I like. So, they just collect this data, its ok.” Xiu Ying’s quote also illustrates how she believes this trade-off is necessary to enable the system to work; in order to get benefits, she needs to give up personal information. Jing makes a similar statement but emphasizes the contractual nature of this exchange: “I think it’s kind of a

68 contract. If you want to use their products you have to give your information, that’s kind of like a contract.” In her research on user attitudes towards (participatory) online surveillance practices, Kirsty Best arrives at a similar conclusion; she states that users of digital technologies often “concentrate […] on their immediate impacts in terms of rewards and punishments” and regard these negative aspects as necessary “trade-offs in exchange for the conveniences of electronic life” (18). Indeed, users of Sesame Credit generally regard the immediate benefits and perceived convenience as a worthwhile, and sometimes necessary, exchange to negative aspects that come with the system. Ming, however, goes one step further by stating he never thought about privacy issues due to the convenience of the application: “Since it’s too convenient, […] I have never actually to be honest recognised the personal information [issues].” As users view Sesame Credit in terms of convenience, the social credit system becomes normalised in everyday usage, along with privacy issues. Instead of a trade-off, the discourse of convenience thus also ‘covers up’ any negative discourses about the technology.

Discourse of societal benefits Most participants expressed their approval of Sesame Credit, and often also the national Social Credit System, in terms of benefits for society. Social credit systems would generally have a positive influence in society or would be able to solve societal problems in China. Talking about the national SCS, Ming says that he thinks “it will help the society become a better society” and believes it will make it more “harmonious”. Ming expresses his concern about the lack of trust between people: “I think it’s very important for me to keep my promise. But actually, many people don’t think like this. I’m not judging them, but I just think it’s... I just think it’s a problem.” He states that although notions such as ‘credit’ and ‘trust’ were traditionally valued in China, nowadays people do not value them anymore. The reason, according to him, is urbanisation. He explains the difference between small villages, where people trust each other because they can hold each other accountable, and cities with large populations, that are made up of ‘strangers’:

That’s maybe especially in the village or a country that is the familiar society. Most of them know each other, or even though I don’t know you, but maybe our parents

69 know each other. So, it’s a familiar society. […] And the society… and it formed many big cities and super cities but it’s stranger cities. And if you do... its stranger, it’s just a stranger society. In the stranger [society] we don’t know to trust a stranger because, because just, if I don’t know, I go outside, and I come across with anyone I don’t know if I can trust him.

Social credit systems, according to him, can be a solution for the decreased levels of trust in society: “most of us hope the society become more safety, more favourable, more good. More good in society. And I think at least in this age, in this time, this system will help.” Yan believes that social credit systems will also restore trust in financial situations and prevent fraud:

But if you use Alipay I think it’s a really good idea because China has so much people and some people, they just rent the debt from the bank and never pay it back and disappear from this country or something. So actually, it’s not fair to anyone who lost a lot of money for the country.

Xiu Ying expresses how social credit systems can be a solution for ‘bad behaviour’ in society.

Yea but I do think for other people it improves their behaviours. So, for example, if they use the Sesame Pay to rent a car or to rent a bicycle. And maybe some people don’t care if there is nothing to measure their credit, they don’t care. They just throw the bicycle into the river, or […] paint on the car. They destroy things. Yea but Sesame Credit which can measure their credit, so their behaviours will be improved. (Xiu Ying)

Ming adds to this that social credit systems might reduce uncivil behaviour on the internet:

Now in China, many people just make very serious or very bad, say very bad words on the internet to criticize some people or criticize some news. They just said very, very harmful words, use very, very harmful words. So, if the credit system works that

70 behaviour maybe let them cut their scores or make them care about what they say on the internet.

Contrary to these statements, Wei disapproves of a national Social Credit System. Instead of focusing on the societal benefits, he reflects on the individual limitations:

I don’t think the government collect all the data is reasonable. Because they just watch all of us. [We] have no privacy. That’s the point. So, every search on internet, every phone call, everything I buy from internet or something they will all know. So, I don’t think it’s good.

By virtue of making society safer, Yan notes that she is ok with increased surveillance: “If you are not a bad person, [you’ve got] nothing to hide. Its good right? So, you’re just good people, good behaviour, doing the good things, so why you need to be afraid?” Here, one function of the discourse of societal benefits becomes clear: improvements of society are used to justify the individual limitations that social credit systems might pose. This is also one of the ideological propositions that was identified in Chapter 1 as part of the credit culture that the Chinese government aims to establish. In fact, this discourse is at the core of Foucault’s theory of biopower: individual discipline is practised in the name of improving the living conditions for the (normative part of) the population.

Discourse of inevitability A third discourse that was used by some of the participants is that of ‘inevitability’, or ‘fatalism’ in Kirsty Best’s words (19). Participants expressed that they accept online payment systems such as Alipay and WeChat Pay because they are currently the standard payment methods with little alternatives. Using cash, for example, for many is not considered an option anymore (Wei). Yan connects this default position of online payment methods as part of China’s rapid technological developments. After stating that “in China the technology goes really fast”, she explains:

71 in China, like, you know, card isn’t working really well. Like, you only have a really short time. [First] the people [were] just using cash. Maybe [after] one or two years they start: ‘oh, we can just use the card’. But [after a] really, really short time we already have WeChat Pay and Alipay. So, it doesn’t really working like credit card stuff, the systems. So… ya and Huabei is like things like credit card for the phone.

Negative aspects of mobile payment systems and Sesame Credit are accepted as inescapable. Ming, for instance, expresses that even if he would refrain from using mobile payment platforms, his data would be recorded anyway:

even though we don’t use the Alipay, we have to shop online, we will… and that information will be recorded by that company. I mean we can buy something in Taobao, and don’t pay with Alipay. We can pay through our bank. But their information is also be[ing] record[ed].

The lack of privacy is often regarded as part of ‘the way things are’ in China. Yan states this firmly when she says: “in China is no privacy”. Qiang attributes this to the fact that China does not have a tradition in privacy protection. When discussing a possible collaboration between Alibaba and the Chinese government, and asking his opinion about it, he states:

In China privacy is, is still got a long way to go, because we don’t have the history of protecting our privacy. But if one day like Alipay selling our information to the government, I would say that, there is nothing you can do.

As users believe that there are no possible alternatives to mobile payment systems such as Alipay and perceive issues of privacy as inescapable in contemporary Chinese society, Sesame Credit is easily accepted in their everyday lives. Of course, China also has a history of state surveillance and diminished privacy for citizens, which may be why users accept commercial surveillance in their everyday lives. Concluding, in this research I have found three main discourses with which users motivate their usage: convenience, societal benefits, and inevitability. The discourse of convenience functions to counterbalance negative aspects such as privacy issues and

72 increased surveillance. As the user’s focus is often mainly on the immediate benefits of social credit systems, the idea of convenience also covers up these negative aspects. The second discourse justifies personal limitations that social credit systems pose in the name of societal benefits. Thirdly, the discourse of inevitability, often coupled with the rapid technological developments in China, excludes the possibility of alternatives or solutions to issues with social credit systems.

73 Conclusion

In this thesis, I have shown how Sesame Credit mediates power relations between users and platform owners Alibaba and Ant Financial and view this against the backdrop of the credit culture that the Chinese government aims to establish and the construction of the national Social Credit System that is to be finished by 2020. At present, Sesame Credit is not the ‘pilot’ for the SCS that is reported by many Anglophone journalistic articles. Although its development was initiated by the Chinese government, Sesame Credit’s licence as a commercial pilot for the national Social Credit System was not extended after the trial period of six months. At the moment it thus functions mainly as a consumer credit system run by Ant Financial, a subsidiary of the Alibaba. Sesame Credit is however, part of the largely state-owned credit bureau Baihang. And, as Chapter 1 further shows, a close connection between Alibaba and the government remains, as the government strongly controls commercial internet platforms. It thus remains to be seen what Sesame Credit’s future role will be for the national Social Credit System. Sesame Credit is part of China’s credit culture, which aims to and encourages a credit economy and increase financial inclusion. Besides this, as a practice of soft power, it promotes financial – but also moral and social – values such as credibility, sincerity, trustworthiness, and integrity. This credit culture, however, relies on the idea that these values can be objectively measured and calculated. Van Dijck calls this an ideology of dataism, which includes a “belief in the objective quantification and potential tracking of all kinds of human behavior and sociality” and “involves trust in the (institutional) agents that collect, interpret, and share (meta)data” (Datafication 198; emphasis in original). This way, Sesame Credit is part of an ideological apparatus that promotes ‘calculable’ values that supposedly improve society as a whole, while at the same time it decreases personal freedoms, such as privacy, by increasing surveillance practices. The second chapter of this thesis shows through an ANT-analysis of the technological actors in Sesame Credit, that this ‘objective quantification’ is impossible. First of all, programming algorithms and selecting data (sources) is never neutral, but reflects the programmer’s norms and values – intentionally or unintentionally (Beer; Gillespie, The

74 Relevance; Richterich). It is also evident that in the case of Sesame Credit, Alibaba’s business strategies are programmed in the calculation of the score and that the Sesame Credit, as a technology of power, promotes consumption via, and participation on, the Alipay platform via disciplinary techniques. The score, however, does not reflect the user’s ‘credibility’ as such, but is rather a probabilistic calculation of the risk that a user poses for Alipay based on the construction of a simulation of the user. Through the process of datafication, real-world situations (financial, social, etc.) are decontextualised and translated into measurable data points. Sesame Credit users become dividuals, assemblages of information, processes, and actions (etc.) that can be deconstructed. Through algorithmic calculation and Big Data analytics, multiple data points – including that of the entire user-base – are reassembled and recontextualised into a ‘new algorithmic identity’ (Cheney-Lippold), a simulation (Best), ‘data double’ (Haggerty and Ericson 606), or ‘digital alter ego’ (Amoore 18) of the original subject. In this process, biases present in the data set are incorporated into the calculation of an individual user’s ‘trustworthiness’. Although this simulation is thus disconnected from the original user, s/he is still disciplined according to it as the score ultimately grants or denies access to certain privileges. Disciplinary power, as Wang argues, also operates through the descriptions of the five categories of data that are calculated into the score. As the exact workings of the algorithms are blackboxed, the descriptions function to communicate desired behaviour to the user. Furthermore, the interface reinforces dataism’s idea of ‘objectivity’ through visual clues: it depicts illustrations that connotate notions such as ‘science’, representing scientific calculation, ‘nature’ signifying ‘neutrality’ and ‘impartiality’. As data is collected from, and the score is implemented in, multiple spheres of life, Sesame Credit becomes a scoring mechanism that encompasses more than financial credibility. It is in this way that Sesame Credit can be understood as a social credit system: as it includes data from social connections, and as Sesame Credit can even become a factor in social relations (e.g. as it is implemented into the dating platform Baihe), Sesame Credit financialises the social. Moreover, following Van Doorn, Sesame Credit is a ‘commensurating machine’ that measures aspects of life in terms of capital, transforming the user into a ‘neoliberal subject of value’ and inserting a “financial logic into everyday conduct” (358). Seen through Foucault’s theory of neoliberalism, the user becomes a ‘homo economicus’, “an entrepreneur of himself” (Foucault, Birth 226).

75 Chapter 3 examined how disciplinary power operates in the everyday practices of users and what their motivations for use are. Basing the ethnography on six in-depth interviews, it shows that Sesame Credit, together with its data sharing practices, has become normalised in the everyday lives of users. Users interact with Sesame Credit directly and indirectly through other functions in Alipay. As such, the credit scoring system becomes a calculating mechanism that operates ‘on the background’ of the app, influencing everyday conduct largely unconsciously. Users also incorporate practices of self-surveillance, self- governance and self-discipline and, as they look for personal benefits, take on a governmentality that is encouraged by Alibaba. Wei’s account also shows that discipline can have severe effects on the lives of users as their financial (and other) opportunities and possibilities are precariously limited, providing a quite literal example of the State Council’s exclamation18 that “if trust is broken in one place, restrictions are imposed everywhere” (China, State Council, Opinions, Part I.1). Although the interviewed users are aware of the implications and generally showed concerns about privacy and increased surveillance, they still used, and sometimes enthusiastically welcomed, Sesame Credit in their everyday lives. I identified three discourses that were used in the interviews to account for their use of the credit system. The first is a discourse of convenience that frames negative aspects of the credit systems as a worthwhile trade-off for the immediate benefits. Secondly, Sesame Credit, and the national Social Credit System, are seen as solutions for societal problems, or for general improvements of society. This corresponds to the ideology of China’s credit culture, in which personal limitations are justified for societal benefit. The third discourse is that of inevitability: problems such as increased surveillance are seen as inevitable developments within China’s rapid technological progress. Concluding, Sesame Credit is a technology of power within China’s credit culture that promotes Alibaba’s business strategies in the everyday lives of users through disciplinary techniques. As such norms – financial but also social or other – that are backed by commercial and political interests become incorporated in the everyday conduct of, and between, users. Discipline, here, is however based on the illusion that values like

18 About the national Social Credit System.

76 ‘trustworthiness’ can be objectively calculated. Individual users are thus disciplined according to their externally constructed data double.

77

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