Mira Vale is a PhD candidate in Sociology at the University of Michigan. Her research examines the relationship between technological innovation and ethical innovation in healthcare, bridging science and technology studies, critical data studies, and economic and medical sociology. Mira’s work has been published in Social Science & Medicine, Medical Anthropology Quarterly, and the Journal of Health and Social Behavior, among other venues. She has been recognized with awards and funding from the National Science Foundation, the National Institutes of Health, the Institute for Citizens and Scholars, and the American Sociological Association.

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Abstract: Recent years have seen a surge of efforts to adapt machine learning techniques for healthcare. Data-intensive tools hold great potential to advance medical discovery and precision, but critics ask how these tools will affect care delivery, medical expertise, and health inequality. This talk investigates this transition within digital psychiatry, a field of research and patient care that uses machine learning and other data-intensive techniques to study mental illness and provide mental healthcare. Drawing on three years of ethnographic research and interviews with digital psychiatry researchers across the United States, I analyze how researchers develop data values, moral sentiments around objectivity, precision, quantification, and automation. While psychiatry has historically emphasized clinical judgment, digital psychiatry shifts the basis of professional authority in psychiatry by valorizing data. As digital psychiatrists seek to make psychiatry scientific, they privilege data modeling and devalue psychiatry’s traditional paradigms like clinical expertise and patients’ self-reports about their symptoms and experiences. Ultimately, I demonstrate how digital data enhances psychiatry’s capacity to produce knowledge while data values narrow psychiatry’s ways of knowing. Amidst calls for an “ethics of AI,” this talk sheds light on how ethics are enacted in practice as they become institutionalized as values, standardized as professional norms, and internalized as intuitions.