K. Kostick-Quenet, I. Cohen, S. Gerke, Bernard Lo, James Antaki, Faezah Movahedi, Hasna Njah, Lauren Schoen, J. Estep, J. Blumenthal-Barby, Glenn I. Cohen, James A. Attwood, L. Williams, Dipl.-Jur. Univ, S. Mcadam
tlooto Summary
An attempt is made to evaluate the impact of data mining on race, ethnicity, and education in the context of health care decision-making in the developing world.
Abstract
Abstract When applied in the health sector, AI-based applications raise not only ethical but legal and safety concerns, where algorithms trained on data from majority populations can generate less accurate or reliable results for minorities and other disadvantaged groups.
Citation format
KOSTICK-QUENET, K., et al. Mitigating racial bias in machine learning. JOURNAL OF LAW MEDICINE & ETHICS, 2022, 50: 92–100.