Mira Vale, "Data Values: Digital Behavioral Data and the Transformation of Mental Healthcare"

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 laws
and official guidance lag behind technological advances. This talk investigates how health
researchers develop rules and norms around the use of data-intensive technologies in the absence
of formal regulation, and how these new ideas are poised to change healthcare for clinicians and
patients alike. Drawing on three years of ethnographic research and interviews, I investigate this
transition within digital psychiatry, a field that uses machine learning and other data-intensive
techniques to study mental illness and provide mental healthcare. I analyze how clinician-
researchers settle norms in digital psychiatry as they develop data values, moral sentiments
around digital data’s objectivity, authoritativeness, impartiality, and scalability. I argue data
values have substantive implications for mental health work and care. While psychiatry has
historically emphasized clinical judgment, digital psychiatry hybridizes expertise in psychiatry as
it valorizes data. As digital psychiatrists seek to make psychiatry scientific, they privilege data
modeling and devalue clinical observation and patients’ self-reports about their symptoms and
experiences. Amidst calls to formalize an “ethics of AI,” this talk sheds light on how ethics get
settled in practice as they become standardized as professional norms and internalized as
intuition.