Message posted on 27/06/2018
Deadline Extension: The Cultural Life of Machine Learning: An Incursion into Critical AI Studies
** Apologies for cross-posting ** <br> <br> <br> <br> <br> <br> <br> <br>The Cultural Life of Machine Learning: An Incursion into Critical AI Studies <br>Preconference Workshop, #AoIR2018 <br>Montral, Canada <br>Urbanisation Culture Socit Research Centre, INRS (Institut national de la <br>recherche scientifique) <br>Wednesday October 10th 2018 <br> <br>Deadline for Abstracts: June 30th 2018 *Extended Deadline*: July 7th 2018 <br> <br> <br>Keynote: Orit Halpern (Department of Sociology and Anthropology, Concordia <br>University) <br> <br>Machine learning (ML), deep neural networks, differentiable programming and <br>related contemporary novelties in artificial intelligence (AI) are all leading <br>to the development of an ambiguous yet efficient narrative promoting the <br>dominance of a scientific fieldas well as a ubiquitous business model. <br>Indeed, AI is very much in full hype mode. For its advocates, it represents a <br>tsunami (Manning, 2015) or revolution (Sejnowski, 2018)terms indicative <br>of a very performative and promotional, if not self-fulfilling, discourse. The <br>question, then, is: how are the social sciences and humanities to dissect such <br>a discourse and make sense of all its practical implications? So far, the <br>literature on algorithms and algorithmic cultures has been keen to explore <br>both their broad socio-economic, political and cultural repercussions, and the <br>ways they relate to different disciplines, from sociology to communication and <br>Internet studies. The crucial task ahead is understanding the specific ways by <br>which the new challenges raised by ML and AI technologies affect this wider <br>framework. This would imply not only closer collaboration among <br>disciplinesincluding those of STS for instancebut also the development of <br>new critical insights and perspectives. Thus a helpful and precise <br>pre-conference workshop question could be: what is the best way to develop a <br>fine-grained yet encompassing field under the name of Critical AI Studies? We <br>propose to explore three regimes in which ML and 21st-century AI crystallize <br>and come to justify their existence: (1) epistemology, (2) agency, and (3) <br>governmentalityeach of which generates new challenges as well as new <br>directions for inquiries. <br> <br>In terms of epistemology, it is important to recognize that ML and AI are <br>situated forms of knowledge production, and thus worthy of empirical <br>examination (Pinch and Bijker, 1987). At present, we only have internal <br>accounts of the historical development of the machine learning field, which <br>increasingly reproduce a teleological story of its rise (Rosenblatt, 1958) and <br>fall (Minsky and Papert 1968; Vapnik 1998) and rise (Hinton 2006), concluding <br>with the diverse if as-yet unproven applications of deep learning. Especially <br>problematic in this regard is our understanding of how these techniques are <br>increasingly hybridized with large-scale training datasets, specialized <br>graphics-processing hardware, and algorithmic calculus. The rationale behind <br>contemporary ML finds its expression in a very specific laboratory culture <br>(Forsythe 1993), with a specific ethos or model of open science. Models <br>trained on the largest datasets of private corporations are thus made freely <br>available, and subsequently dtourned for the new AIs semiotic environs of <br>image, speech, and textpromising to make the epistemically recalcitrant <br>landscapes of unruly and unstructured data newly manageable. <br> <br>As the knowledge-production techniques of ML and AI move further into the <br>fabric of everyday life, it creates a particularly new form of agency. Unlike <br>the static, rule-based systems critiqued in a previous generation by Dreyfus <br>(1972), modern AI models pragmatically unfold as a temporal flow of <br>decontextualized classifications. What then does agency mean for machine <br>learners (Mackenzie, 2017)? Performance in this particular case relates to the <br>power of inferring and predicting outcomes (Burrell, 2016); new kinds of <br>algorithmic control thus emerge at the junction of meaning-making and <br>decision-making. The implications of this question are tangible, particularly <br>as ML becomes more unsupervised and begins to impact on numerous aspects of <br>daily life. Social media, for instance, are undergoing radical change, as <br>insightful new actants come to populate the world: Echo translates your <br>desires into Amazon purchases, and Facebook is now able to detect suicidal <br>behaviours. In the general domain of work, too, these actants leave permanent <br>tracesnot only on repetitive tasks, but on the broader intellectual <br>responsibility. <br> <br>Last but not least, the final regime to explore in this preconference workshop <br>is governmentality. The politics of ML and AI are still largely to be <br>outlined, and the question of power for these techniques remains largely <br>unexplored. Governmentality refers specifically to how a field is organisedby <br>whom, for what purposes, and through which means and discourses (Foucault, <br>1991). As stated above, ML and AI are based on a model of open science and <br>innovation, in which public actorssuch as governments and universitiesare <br>deeply implicated (Etzkowitz and Leydesdorff, 2000). One problem, however, is <br>that while the algorithms themselves may be openly available, the datasets on <br>which they rely for implementation are nothence the massive advantages for <br>private actors such as Google or Facebook who control the data, as well as the <br>economic resources to attract the brightest students in the field. But there <br>is more: this same open innovation model makes possible the manufacture of <br>military AI with little regulatory oversight, as is the case for China, whose <br>government is currently helping to fuel an AI arms race (Simonite 2017). What <br>alternatives or counter-powers could be imagined in these circumstances? Could <br>ethical considerations stand alone without a proper and fully developed <br>critical approach to ML and AI? This workshop will try to address these <br>pressing and interconnected issues. <br> <br>We welcome all submissions which might profitably connect with one or more of <br>these three categories of epistemology, agency, and governmentality; but we <br>welcome other theoretically and/or empirically rich contributions. <br> <br>Interested scholars should submit proposal abstracts, of approximately 250 <br>words, by July 7th 2018 to CriticalAI2018 [at] gmail [dot] com. Proposals may <br>represent works in progress, short position papers, or more developed <br>research. The format of the workshop will focus on paper presentations and <br>keynotes, with additional opportunities for group discussion and reflection. <br> <br>This preconference workshop will be held at the Urbanisation Culture Socit <br>Research Centre of INRS (Institut national de la recherche scientifique). The <br>Centre is located at 385 Sherbrooke St E, Montreal, QC, and is about a <br>20-minute train ride from the Centre Sheraton on the STM Orange Line (enter at <br>the Bonaventure stop, exit at Sherbrooke), or about a 30-minute walk along Rue <br>Sherbrooke. <br> <br>For information on the AoIR (Association of Internet Researchers) conference, <br>see https://aoir.org/aoir2018/ ; for other preconference workshops at AoIR <br>2018, see https://aoir.org/aoir2018/preconfwrkshop/. <br> <br>Organizers: Jonathan Roberge (INRS), Michael Castelle (University of Warwick), <br>and Thomas Crosbie (Royal Danish Defence College). <br>_______________________________________________ <br>EASST's Eurograd mailing list <br>Eurograd (at) lists.easst.net <br>Unsubscribe or edit subscription options: http://lists.easst.net/listinfo.cgi/eurograd-easst.net <br> <br>Meet us via https://twitter.com/STSeasst <br> <br>Report abuses of this list to Eurograd-owner@lists.easst.netview formatted text
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