Coding Faces in Medicine: Exploring Social and Ethical Implications of Medical Facial Recognition
How is facial data turned into medical data? What systems manage medical facial data? What ethical risks come with medical face recognition?
About the Project
The project examines the societal and ethical challenges of facial recognition technology in medicine and healthcare. While increasingly used in areas such as genomic medicine, pain assessment, and psychiatric care—and promoted as a transformative diagnostic tool—it raises critical concerns around trust, accountability, patient privacy, and surveillance. Using ethnography, interviews, and document analysis, the project explores how digital facial data shapes medical decision-making and knowledge production.
The aim is to build an interdisciplinary network that brings together social scientists, philosophers, medical researchers and practitioners, AI and computational experts, and legal scholars to foster collaboration and exchange. At the same time, the project seeks to raise awareness of the diverse risks associated with medical facial recognition technologies and to develop an ethical framework that systematically addresses these challenges. In addition, a short video will be produced that presents a data story on facial data in medical and healthcare contexts, highlighting the ethical stakes by tracing the stages of data collection, processing, storage, and use in diagnostic environments.
About the Fellow
Paul Trauttmansdorff is a postdoctoral researcher at the TUM School of Social Sciences and Technology. He is interested in how bodily data are produced and collected, and how these data are circulated and evaluated for various social and political purposes. He is part of the Ethical Data Initiative.
