Carr-Ryan Center Fellow (2026-27)
Dr. Marie Plaisime is a medical sociologist and public health researcher whose work sits at the intersection of clinical decision-making, technology, artificial intelligence, and human rights. She investigates how inequities embedded in historical data become encoded in algorithmic systems, shape professional decision-making, and ultimately produce unequal outcomes through the very data used to train future technologies. Her research examines these dynamics across healthcare, education, and child protection systems.
As a Research Associate at Harvard University's FXB Center for Health and Human Rights, Dr. Plaisime leads funded research on the social and ethical dimensions of AI in healthcare, child protection, and public health. Her work focuses on the gap between algorithmic design and real-world implementation, examining how organizational constraints, workforce conditions, and institutional structures shape the effects of emerging technologies in practice. Her research has been supported by the National Science Foundation and the National Institutes of Health and has appeared in leading journals including JAMA, The Lancet, and Social Science & Medicine. She is also a Robert Wood Johnson Foundation Health Policy Research Scholar.
Drawing on computational, quantitative, and qualitative methods, Dr. Plaisime evaluates AI-driven systems and develops frameworks for accountable technology governance. Her work bridges research, policy, and practice by examining what frontline professionals need to navigate technology-enabled harms and what institutions require to deploy these systems responsibly and serve the public effectively.
Fellowship Project: Algorithmic tools increasingly shape high-stakes decisions in healthcare, public health, and child protection, yet growing evidence shows they can produce unequal outcomes for vulnerable and underserved communities. This project examines how algorithmic bias and workforce pressures interact to compound disparities across these systems, where children and patients may be simultaneously over-surveilled and under-protected. Drawing on governance documents, large-scale datasets, audit reports, and interviews with key stakeholders, the project develops an evidence-based framework for accountability that treats the interaction between humans and algorithms, not the technology alone, as the central unit of analysis. The project asks what forms of institutional accountability are necessary when algorithmic systems contribute to harm and unequal outcomes.
Fellowship Project: Algorithmic tools increasingly shape high-stakes decisions in healthcare, public health, and child protection, yet growing evidence shows they can produce unequal outcomes for vulnerable and underserved communities. This project examines how algorithmic bias and workforce pressures interact to compound disparities across these systems, where children and patients may be simultaneously over-surveilled and under-protected. Drawing on governance documents, large-scale datasets, audit reports, and interviews with key stakeholders, the project develops an evidence-based framework for accountability that treats the interaction between humans and algorithms, not the technology alone, as the central unit of analysis. The project asks what forms of institutional accountability are necessary when algorithmic systems contribute to harm and unequal outcomes.