Platforms from the Inside-Out

ISSN 2385-2755 DiSSE Working papers [online] Platforms from the Inside-Out Maria Concetta Ambra N. 19/2020 SAPIENZA - UNIVERSITY OF ROME P.le Aldo Moro n.5 – 00185 Roma T(+39) 0649910563 CF80209930587 – P.IVA 02133771002 Platforms from the Inside-Out Maria Concetta Ambra1 Department of Social Sciences and Economics, Sapienza University of Rome Abstract: This article focuses on Amazon Mechanical Turk (AMT), the crowdsourcing platform created by Amazon, with the aim to enrich our knowledge of this specific platform and to contribute to the debate on ‘platform economy’. In light of the massive changes triggered by the new digital revolution, many scholars have recently examined how platform work has changed, by exploring transformations in employee status and the new content of platform work. This article addresses two interrelated questions: to what extent and in what ways does AMT chal- lenge the boundaries between paid and unpaid digital labour? How does AMT exploit online labour to extract surplus value? The research was undertaken from December 2018 and July 2019, through the collection of 50 documents originating from three Amazon web sites. These documents have been examined though the technique of content analysis by adopting the NVivo software. In conclusion, it explains how Amazon has been able to develop a hybrid system of human-machine work. This specific model can be also fruitful used to speed up the machine learning process and to make it more accurate. Keywords: Amazon Mechanical Turk; Crowdsourcing Platform; Digital Piecework; Intellectual Property Rights; Machine Learning. JEL codes: J30, J83, D20, D26, O30. J30 (Wages, Compensation, and Labor Costs: General) J83 (Labor Standards: Workers' Rights) D20 (Production and Organizations: General) D26 (Crowd-Based Firms) O30 (Innovation; Research and Development; Technological Change; Intellectual Property Rights: General) 1 Post doc researcher at Department of Social Sciences and Economics, Sapienza University of Rome; E-mail: [email protected] 1. Introduction Contemporary work is increasingly influenced by digitalisation and technological innovations, including the development of Artificial Intelligence. The contents and nature of work, as well as the organisation and management thereof, are greatly affected by technology as well as the massive gen- eration of data caused by the spread of algorithms. Furthermore, platforms and crowd working are growing by enabling “firms to employ a digital, flexible and scalable workforce on a global scale that sits outside the traditional boundaries of labour laws and regulations” (Bergvall-Kåreborn and Howcroft 2014). This article focuses on Amazon Mechanical Turk (AMT), the Amazon crowdsourcing platform matching supply and demand of micro-work. AMT is a highly relevant case study due to the large number of workers involved. According to Amazon, this workforce is over 500 thousand workers2 while Difallah et al. (2018) demonstrate that these workers are at least 100 thousand with more than two thousand active at any given time. This analysis of AMT workers, based on the results of a survey conducted over a period of 28 months, illustrates an updated composition of the AMT workers population across demographics variables such as country, gender, age, income and marital status3. It is worth noting the potential of AMT, which is a technically usable platform anywhere in the world. According to Difallah et al. (2018) it is currently active mainly in the United States (75%), India (16%) and Canada (1.1%) and less so in Europe, with Great Britain first (0.7%) followed by Germany (0.27%) and France and Italy both just above 0.20%. However, researchers noticed around May 2016 a notable effect in sharp dropping of the percentage of US workers on AMT and the increase of international workers from Canada, Great Britain, and other countries. Most likely, various factors hinder the spread of AMT in the European market and in Italy. First of all the language used. Today, almost all available micro jobs require the knowledge and com- petence of the English language. A second problem could be the different forms of payment. While U.S. workers can transfer their earnings directly to their bank accounts, European workers, on the other hand, cannot, and Amazon pays them back with gift card to spend on Amazon.com. This can adversely affect the platform's attractiveness to potential European and Italian turkers. In this regard, it is interesting to note that recently, in May 2019, Amazon announced a new feature that allows "turkers outside the United States to transfer their earnings to a virtual US bank account through a third-party paid service provider". This new solution, available for 25 countries and for 2 Source: AWS_File 8, November 28 2018. 3 Turkers are a generally balanced workforce, with 51% female workers and 49% male. Mainly younger; 40% of the workers report being single and 42% report being married; another 10% reports cohabitating, 5% being divorced, and 3% being engaged. MTurk workers have household incomes that are below the average US population (Difallah et al. 2018). 2 Italy, has been adopted to responds to requests from "thousands of international workers, which have expressed that they would prefer to receive earnings in their local currency4. With these aspects in mind, this article aims to shed light on two related issues. The first concerns workers' conditions, pay and the growing relationship between the performance of this online work and the increase in unpaid activities. The second regards the specific mechanisms through which turkers contribute to the creation of surplus value through their platform work. The article is structured as follows: par.2 provides a general overview of the main literature on AMT; par. 3 illustrates the method and research techniques adopted here. Section 4 focuses on working conditions within the AMT, emphasizing how turkers contribute to the value creation process. Par. 5 highlights the links between AMT and AWS showing how Amazon has also been able to exploit human intelligence to develop artificial intelligence, greatly increasing its profits. 2. Literature review By using a Crowd Platform and a virtual network, firms can outsource to an undefined pool of digital workers those functions once performed by internal employees or offshored to low-cost geographies, thus shifting costs and offloading risks (Bergvall-Kåreborn &Howcroft, 2014). There is an emerging critical literature, which clusters around Amazon’s Mechanical Turk as an example of labour exploitation (Howcroft & Bergvall-Kareborn, 2018). According to (Lehdonvirta, (2016), from the perspec- tive of capital, voluminous crowds can process large quantities of data in a short time frame, enabling the exploitation of geographical differences in skills and labour costs. The availability of low-cost work attracts consumers, allowing the platform to grow rapidly, strengthen the brand and generate higher market ratings. Crowdsourcing platforms maintain strict control over the nature and contents of the work, rate performance and payment, while at the same time they can eradicate all human contact (Irani 2015; Graham at. al. 2017) thus obscuring “the pivotal role played by labour, employment relations and the exploitative working conditions, which underpin it” (Bergvall-Kåreborn &Howcroft, 2014). Platforms can capture and create value with the sourcing of unregulated and unprotected labour/expertise (Katz, 2015), by letting a flexible and scalable workforce outside the traditional boundaries of labour laws and regulations (Bergvall-Kåreborn &Howcroft, 2014). Many analyses outlined how crowdsourcing allows companies to lower labour costs without any significant obligation regarding labour regulation, welfare benefits and intellectual property rights (Irani 2015; Cherry 2016; 2017). The cumulative impact of work-related changes such as casualisation, informalisation and demutual- isation of risk (de Stefano, 2016) means that increasing numbers are attracted to crowd work. While some participate for additional earnings, others relies on digital platforms as their primary source of income (Berg, 2016). In terms of the employment contract, the majority of these platforms classify external contributors as ‘independent contactors’ (Smith and Leberstein, 2015; De Stefano 2016; Berg, 2016) with self-employed status. This provides tax advantages for platforms and alleviates the regulatory requirements of paying minimum wage (Felstiner, 2011), while contributors shoulder per- sonal liabilities. Bogus self-employment represents a process of legal engineering that shifts risk onto workers who are unprotected by minimum wage legislation or any other workplace entitlements. In 4 Source: BlogMTurk_Workers Outside US, File 48, May 1, 2019. this respect, crowdsourcing feeds the worrying phenomenon of non-standard work (Eurofound, 2015). This raises concerns regarding the regulation of these working activities, which lack any basic social security contributions and welfare entitlements such as holiday pay, sick leave, and insurance programs including group health insurance or retirement benefits, or any compensation benefits in the event of injury or illness. Moreover, many analyses have shown how the ruthless competition on platforms often pushes earnings so low that workers are compelled to work longer in order to earn a decent income (De Stefano 2016). According to Hara et al. (2015), the use of asymmetric rating systems

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