Colloquia

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Abstract: AI technology is currently developed and deployed in the U.S. at an unprecedented pace,generating important social, political, and economic consequences but guided by little to nopublic input. How can the public shape AI technology and its growing influence in their lives? Inthis talk, I will draw on two studies to explore a set of dynamics that define current AI policydebates: (1) the continuing dominance of a market-based approach to policymaking that defers totech firms and does little to check their growing power, and (2) the new proliferation of publiclyaccessible AI expertise that largely promotes individual consumerism. As I will show, ensuringthat the development and application of AI technology is democratic and equitable will requiredeeper shifts in the logic of policymaking and the practice of expertise, with distinct implicationsfor universities and social scientists. A Brief Bio:

Panel Discussion: “Teaching and Researching Race, Ethnicity, and Immigration in the Current Moment” feat. Cristina Mora, Michael Rodríguez-Muñíz, andJenna Nobles (UC Berkeley Demography)  Moderated by Cybelle Fox 

ABSTRACT:  Millions of economically precarious U.S. workers live at the beck and call of their employers. In retail and food service industries, low wages and insufficient work hours relegate workers to a life on call, perpetually at risk of being added to or dropped from constantly changing work schedules. This talk weaves together in-depth interviews with original survey data from over 100,000 service sector workers to paint a portrait of dehumanized scheduling, in which employers assign schedules with little regard for human needs for rest, consistency, or advance notice to allow for planning.

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Recent years have seen a surge of efforts to adapt machine learning techniques for healthcare.These tools have provoked heated debates about privacy, safety, bias, and inequality, but lawsand official guidance lag behind technological advances. This talk investigates how healthresearchers develop rules and norms around the use of data-intensive technologies in the absenceof formal regulation, and how these new ideas are poised to change healthcare for clinicians andpatients alike. Drawing on three years of ethnographic research and interviews, I investigate thistransition within digital psychiatry, a field that uses machine learning and other data-intensivetechniques to study mental illness and provide mental healthcare. I analyze how clinician-researchers settle norms in digital psychiatry as they develop data values, moral sentimentsaround digital data’s objectivity, authoritativeness, impartiality, and scalability. I argue datavalues have substantive implications for mental health work and care. While psychiatry hashistorically emphasized clinical judgment, digital psychiatry hybridizes expertise in psychiatry as

Gender/sex is in AI. Sociologists and tech critics alike have written extensively about the consequences of it being there. Many technologists and not a few scientists celebrate its presence. But how exactly do gender, sex, and their ambiguous conflations enter into ‘AI’? This talk develops an account of analytically distinct sociotechnical processes by which social facts such as gender/sex become embedded in computer systems, with a focus on contemporary machine learning. As I will show, individual agency, social (infra)structure, and the logic of learning algorithms themselves all have roles to play.