On algorithmic fairness in medical practice

Cambridge Quarterly of Healthcare Ethics 31 (1):83-94 (2022)
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The application of machine-learning technologies to medical practice promises to enhance the capabilities of healthcare professionals in the assessment, diagnosis, and treatment, of medical conditions. However, there is growing concern that algorithmic bias may perpetuate or exacerbate existing health inequalities. Hence, it matters that we make precise the different respects in which algorithmic bias can arise in medicine, and also make clear the normative relevance of these different kinds of algorithmic bias for broader questions about justice and fairness in healthcare. In this paper, we provide the building blocks for an account of algorithmic bias and its normative relevance in medicine.

Author Profiles

Thomas Grote
University of Tuebingen
Geoff Keeling
Stanford University


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