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