Hi Stephen, Many thanks for your question and for rising this pressing issue (i.e. that we social scientist might need to work faster and better on AI and ML). While I would mostly follow Professor Burrell comment, I would also like to point out how there’s two intertwined bodies of literature here. One deals with the last 5 years or so of what has been written on algorithms and algorithmic cultures. Atop of the mostly American list provided, I would suggest works coming from David Beer, Rob Kitchin or our very own, Robert Seyfert and I’ book on Algorithmic cultures. (see https://www.routledge.com/Algorithmic-Cultures-Essays-on-Meaning-Performance...). The second body of literature is indeed smaller as it particularly deals with Artificial intelligence and all of its latest twists (ML, Deep Learning, etc.). I would once more agree with Professor Burrell that Adrian Mackenzie' new book has good chance of becoming the equivalent of the important, but maybe more philosophical book on Superintelligence by Nick Bostrom. Of late, I’ve also come across several interesting pieces (some new, some rather old or outdated but that could serve as a sort of archeology). Here they are: Carley, K. M. (1996). Artificial Intelligence within Sociology. Sociological Methods & Research, 25(1), 3 30. https://doi.org/10.1177/0049124196025001001 Dreyfus, H. L., & Dreyfus, S. E. (1986). Mind over machine: the power of human intuition and expertise in the era of the computer. New York: Free Press. Fox, S. (2016). Domesticating artificial intelligence: Expanding human self-expression through applications of artificial intelligence in prosumption. Journal of Consumer Culture, 146954051665912. https://doi.org/10.1177/1469540516659126 Gunkel, D. J. (2012). Communication and artificial intelligence: Opportunities and challenges for the 21st century. communication+ 1, 1(1), 1–25. Hoffman, S. G. (2017). Managing Ambiguities at the Edge of Knowledge: Research Strategy and Artificial Intelligence Labs in an Era of Academic Capitalism. Science, Technology, & Human Values, 42(4), 703 740. https://doi.org/10.1177/0162243916687038 Natale, S., & Ballatore, A. (2017). Imagining the thinking machine: Technological myths and the rise of artificial intelligence. Convergence: The International Journal of Research into New Media Technologies, 135485651771516. https://doi.org/10.1177/1354856517715164 Stilgoe, J. (2017). Machine learning, social learning and the governance of self-driving cars. Social Studies of Science, 030631271774168. https://doi.org/10.1177/0306312717741687 Tripathi, A. K. (2017). Hermeneutics of technological culture. AI & SOCIETY, 32(2), 137 148. https://doi.org/10.1007/s00146-017-0717-4 My apologies for all the typos; I’m a Francophone who doesn’t like working on Friday afternoon that much !!!:) Best, JR Jonathan Roberge Professeur-chercheur agrégé Titulaire de la Chaire de recherche du Canada sur les Nouveaux Environnements Numériques et l'Intermédiation Culturelle (NENIC Lab) Institut national de la recherche scientifique Centre Urbanisation Culture Société 490, rue de la Couronne, Québec, Qc., Canada, G1K 9A9 Tél. 418-687-6401, fax. 418 687-6425 ________________________________________ De : Air-L [air-l-bounces@listserv.aoir.org] de la part de Jenna Burrell [jenna1@gmail.com] Date d'envoi : 12 janvier 2018 12:25 À : Yosem Companys Cc : Stephen Paff; STSGRAD@googlegroups.com; Science & Technology Studies; AIR Objet : Re: [Air-L] Any sociological or STS research on machine learning? Hi Stephen and AIR-L, Yes, there's a lot of work by sociologists and STS researchers on machine learning, including books published in the last year or about to come out... Virginia Eubanks book *Automating Inequality: How High-Tech Tools Profile, Punish and Police the Poor* is about to come out. I believe it's an ethnography and that it deals, at least in part, with applications of machine learning (in areas like predictive policing). There's a new book out by STS scholar Adrien Mackenzie *Machine Learners* - https://mitpress.mit.edu/books/machine-learners Also look at what Nick Seaver has written. He has an ethnography coming out on music recommendation systems/algorithms (http://nickseaver.net/) Malte Ziewitz did an ethnography of the search engine optimization industry and has done lots of work in this space - http://zwtz.org/ Marion Foucade has a deeply sociological read on this topic and has written a great piece about the "mechanisms" that produce "classification situations" which are consequential to life circumstances (she doesn't use the phrase machine learning, but certainly ML compose some of the underlying 'mechanisms' she is concerned with) - http://www. sciencedirect.com/science/article/pii/S0361368213000743 I've also written something in this space: "How the machine ‘thinks’: Understanding opacity in machine learning algorithms" http://journals.sagepub.com/doi/abs/10.1177/2053951715622512 - I'm a sociologist and an ethnographer, though this particular piece isn't ethnographic. This list just scratches the surface ... there's just so much work coming out in this space so I'll just offer some names of other people to look into: Solon Barocas, Karen Levy, Kate Crawford, Christian Sandvig, Tarleton Gillespie, Angele Christen, Mike Ananny, Nick Diakopolous, Luke Stark. Plus lots of people doing work in this space at Data & Society ( https://datasociety.net/). Jenna Burrell Associate Professor School of Information UC-Berkeley On Fri, Jan 12, 2018 at 8:29 AM, Yosem Companys <ycompanys@gmail.com> wrote:
From: Stephen Paff <stephen.paff@gmail.com>
Hello everyone,
I am conducting research into the anthropology of machine learning. Does anyone know of ethnographies of the development, implementation, and/or use of machine learning algorithms? Are there any sociologists, STS researchers, or scholars from other related fields studying machine learning whose work I should look into as well?
Sincerely, Stephen Paff _______________________________________________ The Air-L@listserv.aoir.org mailing list is provided by the Association of Internet Researchers http://aoir.org Subscribe, change options or unsubscribe at: http://listserv.aoir.org/ listinfo.cgi/air-l-aoir.org
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