Speculative Diagrams: experiments in mapping Youtube.
1 2 Betti Marenko , David Benqué Abstract: The notion that the future has a ‘shape’ is a 1 Central Saint Martins Uni- deep-rooted construct, the cornerstone of how chance versity of the Arts Lon- is mediated, for example through the ‘distributions’ don, United Kingdom of probability theory. Algorithmic prediction, via [email protected] machine learning, builds on these shapes and ampli- 2 School of Communica- fies their complexity and authority. While the problem- tion, Royal College of Art, atic effects of this predictive regime and the preemp- London, United Kingdom tive politics it supports are objects of concern for scholars and practitioners across the humanities, social sciences, art, and philosophy, design is surpris- ingly disengaged from this conversation. Instead, it is either concerned with data visualization, often without questioning its positivist ontology, or with ‘seamless’ non-interfaces which effectively seek to remove choice. Against this backdrop, our proposal brings together design theory and design practice to inter rogate current modes of algorithmic prediction and the construction of subjectivity enabled by ‘choice design’. Our designed artifacts are diagrams to think through practice about the shape(s) of the possible. Rather than designing predictable futures, we aim to use diagram-making to expose and reframe choice design. These design artifacts - initial and ongoing experiments in mapping YouTube recommendations - are a series of computational diagrams that weave together the tools of computational prediction, critical design practice, and theory.
Keywords: algorithmic prediction; diagrams; recommendation; criti- cal data visualization;
1 3 Marenko, Benqué | Architectures of Choice Vol.1 Youtube. Vol.1 Choice of Benqué | Architectures Marenko, 2 #rtd2019 #researchthroughdesign #delft #rotterdam
Frictions and Shifts in RTD - - - them (Cheney-Lippold, 2011). 5 rounding YouTube recommendation system, and its lack of account and its lack of system, recommendation rounding YouTube it from the outside: to ‘map’ initiatives has prompted several ability, techniques 2017), ‘manual’ (Albright, engineering strategies reverse project designed the AlgoTransparency notably 2018), and most (Lewis, the 2016 US presiden- after to expose the bias in recommendations tial election (Chaslot et al, 2016, Lewis and McCormick, 2018). on in order to hypothesise data/evidence seek to gather These examples three use net All might be producing detrimental effects. the system how channels or individual vid- as nodes model which (e.g. data graphs, work suited well are networks While recommendations). (e.g. relations and eos) - system recommendation such as vast of to mapping the the complexity - - their claim to provid to model such data way the only available arguably ‘transparency’ sense of ing any all. The hand crafted does at well not scale a small number a complex actors usedgraph of of Lewis (2018) shows by When readable. co-appearances of web in videos and remains relatively ’s AlgoTransparency in its totality however, to map Youtube attempting bowl” or “spaghetti Map becomes nothing more than a “hairball” Youtube - hardly revelatory behind the processes about them (Bounegru et al., Seeing, opacity. a sort visibility-rich of 2017), in fact counter-producing 2016). and Crawford, (Ananny knowing is not necessarily in this case, 2. Mapping YouTube and the recommendations, YouTube architecture of The intricate posed by content, are manipulated challenges of its sheer volume by is promoted which type content of engineering decisions concerning et al, This is a notoriously opaque 2016). (Covington on the platform (partlyprocess non-disclosure because of the engi- for agreements) the how only limited of understanding who have neers themselves predicts 2016). The secrecy sur - let alone the public (Burrell, system 1. Algorithmic governmentality: governmentality: 1. Algorithmic prediction ontology of calculative the a by generated Every recommendations a series of offered time a user is are being - they or YouTube Amazon Facebook, - Google, digital platform ana- by operates a technology on by that algorithms) (ranking worked (Covington future behaviours to forecast on past behaviours data lysing It orients this. is more than analytics predictive et al, 2016). However, media describes Mark Hansen theorist What as potential tendencies. (Hansen, 2014, 38) – the im- ontology prediction” “the of calculative and on any experience human pact of technomedia of on the texture designing tendencies and of way subsequent – is a systemic behaviour - are not fully accessi that in ways level, a precognitive propensities at Our forthcoming awareness. and perceptual cognition ble to conscious are predictedbehaviours happen, not simply before they but before of evaluation the making them happen. think of even might Thus, we build- also their role in are designed concerns choice of systems how regimes of calls Rouvroy Antoinette philosopher ing identities - what - recommen 2016). This is how (Rouvroy, governmentality algorithmic they next, watch to what suggest or Netflix when Youtube work: dations social a on generate identities drawn but not only build user profiles, turns into prediction In other words, fragmented points. multitude of from a neutral and categories extract do not . Classifiers prescription standpoint, createobjective but actively construe is used capturedThe granularity of to recursively user data our every of (and obscure) a live-feed single subject position. simul- By continuous is a this process taneously reflecting back and building up, endlessly sets, data discrete the subject into recursive of modulation in his Gilles Deleuze points - what access distributed on a multitude of 1995, 180). (Deleuze, the ‘dividual’ calls Societies on Control Postscript 4 Figure 1. Youtube “Up Next” recommendations for the vid- eo “Saying Logan Paul 100,000 Times” by MrBeast. Screen capture by the authors on 19.10.2018, url: https://www. youtube.com/watch?v=_FX6rml2Yjs - - - - - – are Youtube Vol.1 Choice of titled Architectures recommendations, Introduction #rtd2019 #researchthroughdesign #delft #rotterdam Our collaboration between theory design and collaboration Our (Marenko practice and and the practice criticises complicates/ investigates/ 2018) Benque, prediction:politics algorithmic future-building of machine learn via - count ing. to position It sets out as a computational diagramming er-practice, to reclaim the possible from algorithmic capture. We use We capture. the possible to reclaim from algorithmic er-practice, unformed mapping the and the visualization - speculative diagrams 2016) - (O’Sullivan, situations feed into changeable as they unstable and theorise prediction design from a to think through, manipulate, of ways affords that as a practice use diagram-making standpoint. We modes through probing the possible prediction as it emerges of and In this a solid material. as fluid, rather taken on a future speculating case from this starting point practical paper into our first move we platform. the Youtube on recommendations of a mapping study: or not, an order explicitly media of is, interface to a catalogue Any to present While this is inescapable—there is no way choices. ing of While is also problematic. ordering—it without some kind of a list their interfaces appearuser-friendly choices, to be about offering to more likely content “toward underlying goal is to direct viewers that (Arnold, and subscribed” engaged them 2016, 99). Systems keep advertis for capture (eyeballs aim to maximise user “engagement” - (Coving “fresh” of foregrounding ing) to the continuous encourage ton et al, (Jiang et al, 2016) and “viral” 2014)—this has been content of traumatising from the detrimental effects, hugely to have shown ideol- 2017) to the spreading nationalist white of children (Bridle, 2015) shows, (Shapiro, design” 2018) . As “anticipatory ogy (Lewis, options on the of result in the removal design may the idea choice of making decisions on users’ behalf of for—allegedly—their pretext of reliev the notion benefit.convenience, own of Behind a rhetoric masks a political- project. fatigue” con Our ing users from “decision over the need an ordering system—with is not to challenge for cern 400 hours uploaded every minute and 1.8 billion logged-in monthly interface some is required it is hard to argue that users on YouTube the pol - examine to critically to parse want Instead this content. we predictitics embedded “what that to watch systems in the design of next” use this critique to underpin our design interventions. and - The designed artefacts proposed in mapping You – experiments here Tube design’. ‘choice of a visual understanding toward work a programme of are being choice architectures of how to show Their aim is twofold: impacting- algorith on the construction of designed everyday, in our to the attention mic identities (Cheney-Lippold, and to draw 2011); needed them through studying (and negotiations for) of complexities are framed These experiments three theoretical by practice. a critical to foreground perspectives: governmentality the idea algorithmic of 2016); ideas concerning design (Rouvroy, the politics implicit in choice methodological friction from to extract as a way maps and diagrams 1988); a critique and Guattari, (Deleuze mapping systems existent through an assess with these issues engagement lack of design’s of 2011). (Drucker, visualization the positivist ontologyment of data of
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Lo #rtd2019 #researchthroughdesign #delft #rotterdam We approach the space of Youtube mapping by building on, building by mapping and being approachmind Youtube of the space We 3. Designed artefacts: speculative diagrams diagrams speculative artefacts: 3. Designed recommendations YouTube mapping for Collection (Selenium) to simulate with a headless are collected browser Data - with web-pagesuser interaction through recommenda and ‘click’ develop browser’s quick prototyping using the for This allows tions. (Neo4j) where are stored database in a graph data The collected edges by and recommendations videos are represented nodes, by them. and in- is logged with a time stamp Each session connecting the type probe of dicates used. then be can queried This database to return particular or sub-graphs sessions visualisation. for - use au here and discuss prototypes The show we insights. these ful of, as recommendations and visualise store techniques to collect, tomated attempt that diagrams as explorative be must taken They data. network be It must the clarified that capture. algorithmic the logics of to expose our interventionsaim of solutions to the shortcomings is not to claim is to open the aim enquiry of up paths - con that Rather, outlined above. on evidencing the challeng- and to keep apparatus front the predictive es embedded happens. in the tools through which this confrontation er tools to identify parts are interested in collecting we the page of with its homepage, or interacting with. Starting from the YouTube and re links to video pages follow we new videos and topics, of list ‘probe’ designs different Three recommendations. list cursively ways: the breadthbalance the mapping in different and depth of for a giv all recommendations follow to [fig.attempts 2.a.] ripple.py approach very was time This exhaustive en number recursions. of became (see below) consuming, and the resulting visualisations simpler approach recursion. first the A drastically after unreadable at which selects only one video [fig.2.b.] in simple_digger.py is taken a and repeats for list the process random in the recommendations as it This approach also unsatisfactory, was number times. of given presented na- the interconnected single paths which fail to capture the previous exper made evident by the recommendations ture of of the captures all compromise which a is [fig.2.c.] Digger.py iments. each before random. choosing at step one at recommendations c.
Frictions and Shifts in RTD 2018-07-13 14:29:51 2018-07-13 Media isp: Virgin loc: England (n=1 + 1) type: ripple Visualisation of two of two 5. Visualisation Figure Python using sessions ripple.py matplotlib. and networkx packages - recommen levels of two a. shows the Youtube dations from homepage. the first level b. shows followed of recommendations pick. by one random a. b.
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