Research

My dissertation and book project, Data Values: Moral Entrepreneurship in Digital Health, examines ongoing efforts to build an ethics of AI. Specifically, I study the creation of moral rules that govern digital surveillance for mental healthcare. Researchers increasingly turn to technologies like smartphone apps and wearable devices to produce data for healthcare, but these tools have also provoked heated debates about privacy, data ownership, and algorithmic bias. Formal regulation lags behind, giving researchers wide latitude to establish norms and values of their field. 

To understand this moral self-regulation, I completed a year of ethnographic fieldwork with academic teams developing technology for mental healthcare. I argue researchers’ moral rulemaking is a form of entrepreneurship. Moral entrepreneurship shapes the field of digital health by setting what it focuses on, what it neglects, and whom it serves. As my dissertation analyzes this process, it helps explain how moral ideas are adjudicated amidst uncertainty and it uncovers new mechanisms for health disparities and social inequality in our digital future. 

My dissertation is funded by several competitive national fellowships, including the ASA/NSF Doctoral Dissertation Research Improvement Grant and the Institute for Citizens and Scholars’ Charlotte W. Newcombe Dissertation Grant. 

A second project theorizes how algorithmic systems like electronic health records (EHRs) yield real-world consequences, using the case of decision-making processes for prostate cancer care. 

For this project, I completed 20 months of ethnographic research and interviews in a urologic cancer clinic. My major paper from this project addresses a dilemma in the social studies of algorithms literature: on the one hand, algorithmic systems like the EHR are commonly described as sociotechnical systems. On the other hand, it is often argued that algorithms have durable features, for example that algorithms are opaque. To square these stances, I adapt the classic STS concept of affordances to theorize algorithms’ capacities like opacity as relationally constructed. By analyzing how clinicians and patients interact with the EHR to make cancer care decisions, I demonstrate how affordances - the relational aspects of a technology that frame what can happen through its use - permit an understanding of algorithmic opacity as relationally constructed, dynamic, and contextual. 

This paper has been awarded best paper prizes from the Science, Knowledge, and Technology section and the Economic Sociology section of the American Sociological Association. It is currently under review.

My third project studies the role of information technology in efforts to standardize care for health equity. 

This project analyzes how clinicians use EHRs to document data about social determinants of health (SDOH). The study draws on a novel comparison of ethnographic observation at a primary care clinic, interviews with patients, and content analysis of patient medical records. I uncover three types of “local standards” clinicians enact to integrate social data into their care. First, clinicians document SDOH using “signal phrases,” keywords and short sentences that help them recall patients' social stories. Second, clinicians use other technology or face-to-face conversations to share about patients' SDOH with colleagues. Third, clinicians fold discussion of SDOH with patients into their personal relationships. As federal agencies seek to mandate the inclusion of social data in health information technology, this paper illuminates both challenges and opportunities for the standardization of social data in EHRs.

A paper from this study is published in Social Science & Medicine.