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Special Collection

Operationalising Fairness in Medical Algorithms

From machine learning to artificial intelligence, data science is expected to transform healthcare, through rediscovered and newly created algorithms, improved computing power and, most importantly, the availability of bigger and increasingly reliable data with which to train these algorithms. However, numerous critical ethical issues have been identified, spanning privacy, data protection, transparency and explainability, responsibility, and bias. It is widely recognised that many of the machine learning models and tools may have discriminatory impact thereby inadvertently encoding and perpetuating societal biases, thereby contributing to health inequities. Machine learning algorithms should not be focused solely on accuracy, but should also be evaluated on how they might impact disparities in patient outcomes. This special issue brings together the growing community of healthcare practitioners, social scientists, policymakers, engineers and computer scientists to design and discuss practical solutions addressing algorithmic fairness and accountability.
Editorial Operationalising fairness in medical algorithms Parbhoo S, Wawira Gichoya J, Celi LA for MIT Critical Data, et al 10.1136/bmjhci-2022-100617 Original research Identifying undercompensated groups defined by multiple attributes in risk adjustment Zink A, Rose S 10.1136/bmjhci-2021-100414 Can medical algorithms be fair? Three ethical quandaries and one dilemma Bærøe K, Gundersen T, Henden E, et al 10.1136/bmjhci-2021-100445 Resampling to address inequities in predictive modeling of suicide deaths Reeves M, Bhat HS, Goldman-Mellor S 10.1136/bmjhci-2021-100456 Evaluating algorithmic fairness in the presence of clinical guidelines: the case of atherosclerotic cardiovascular disease risk estimation Foryciarz A, Pfohl SR, Patel B, et al 10.1136/bmjhci-2021-100460 Operationalising fairness in medical AI adoption: detection of early Alzheimer’s disease with 2D CNN Heising L, Angelopoulos S 10.1136/bmjhci-2021-100485 Investigating for bias in healthcare algorithms: a sex-stratified analysis of supervised machine learning models in liver disease prediction Straw I, Wu H 10.1136/bmjhci-2021-100457 Review A proposal for developing a platform that evaluates algorithmic equity and accuracy Cerrato P, Halamka J, Pencina M 10.1136/bmjhci-2021-100423 Communication Global disparity bias in ophthalmology artificial intelligence applications Nakayama LF, Kras A, Ribeiro LZ, et al 10.1136/bmjhci-2021-100470

Guest Editors

Leo Anthony Celi, Harvard Medical School Miguel Angel Armengol de la Hoz, Regional Ministry of Health of Southern Spain Sonali Parbhoo, Harvard University Judy Wawira Gichoya, Emory University