Katija Ferryman

Dr Kadija Ferryman

Industry Assistant Professor, NYU Tandon School of Engineering
New York

Dr. Kadija Ferryman is a cultural anthropologist and bioethicist who studies the social, cultural, and ethical implications of health information technologies. Specifically, her research examines how genomics, digital medical records, artificial intelligence, and other technologies impact racial disparities in health. She is currently Industry Assistant Professor at New York University’s Tandon School of Engineering. As a Postdoctoral Scholar at the Data & Society Research Institute in New York, she led the Fairness in Precision Medicine research study, which examines the potential for bias and discrimination in predictive precision medicine.

She earned a BA in Anthropology from Yale University, and a PhD in Anthropology from The New School for Social Research. Before completing her PhD, she was a policy researcher at the Urban Institute where she studied how housing and neighborhoods impact well-being, specifically the effects of public housing redevelopment on children, families, and older adults. Ferryman is a member of the Institutional Review Board for the All of Research Program, a Mozilla Open Science Fellow, and an Affiliate at the Center for Critical Race and Digital Studies. Dr. Ferryman has published research in journals such as Paediatric and Perinatal Epidemiology, the Journal of Health Care for the Poor and UnderservedEuropean Journal of Human Genetics, and Genetics in Medicine. Her research has been featured in multiple publications including Nature, STAT, and The Financial Times.


Listen to Kadija talk about the usefulness of anthropological frameworks to see through power hierarchies. She also shares stories from her work in which social science often merges with more technical fields, to produce results that help make data more ethical.

Kadija Ferryman & Laura Sobola on Multidisciplinary Collaboration, AI, and Machine Learning in Health


Kadija’s talk will focus on the recent US federal policy guidance for developing machine learning tools in medicine. She will examine how this policy guidance fails to address the ways that health data reflect inequities, and she proposes modifications that can prevent group harms and advance more just health technology policy. 

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