Lab Studies Reloaded? Machine Learning, Ethnography, and Critical STS (cfp, 4S, panel 88)
Lab Studies Reloaded? Machine Learning, Ethnography, and Critical STS
Laboratory studies have played an important role in the shaping of STS. Yet, however initially promising, this analytical genre progressively dwindled, becoming the object of recurrent critiques (e.g., Doing 2008; Hess 2001). Those critiques in turn have contributed to an increasing fragmentation, virtually “blowing up” (Lynch 2018) the category of ethnography in STS, now ranging from large-scale assessments of ailing infrastructures to video-based micro-studies of lab bench interactions. This panel takes stock of the situation and asks which role(s) STS lab studies may come to play in the light of a new development, namely the recent revival of machine learning (ML) and attendant promises of ubiquitous artificial intelligence (AI). In particular, the panel addresses three sets of interrelated questions: First, how might the fragmented character of lab studies today be brought to bear on a multifaceted yet cogently articulated ethnography of AI/ML? Second, what difference do ethnographies of “AI at work” make, as they draw upon participant observation, reverse engineering, or video analysis of its situated practices, in addition to the documentary analysis of textbooks, readymade algorithms, or scientific publications? Third, what might be the critical implications of lab studies reloaded, as renewed empirical studies of AI/ML in situ? How might they contribute to regenerating “critical technical practice” (Agre 1997) in and across, if not beyond, STS? To tackle these and related questions, the panel invites both empirical and conceptual contributions.
Thank you for submitting your contribution and/or advertising this open panel, Panel 88 at the upcoming 4S conference in New Orleans, 4-7 September 2019.
Submission deadline is 1 February 2019 (see https://www.4s2019.org/accepted-open-panels/)
Looking forward to reading you or your colleagues!
With best wishes, and apologies for cross-postings
Florian Jaton, Mines ParisTech
Philippe Sormani, Université de Lausanne
Michael Mair, University of Liverpool
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