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On Algorithmic Fairness in Medical Practice

Published online by Cambridge University Press:  20 January 2022

Thomas Grote  and
Geoff Keeling
Thomas Grote*
Affiliation:
Ethics and Philosophy Lab, Cluster of Excellence: Machine Learning: New Perspectives for Science, University of Tübingen, Tübingen, Germany
Geoff Keeling
Affiliation:
Leverhulme Centre for the Future of Intelligence, University of Cambridge, Cambridge, UK
*
*Corresponding author. Email: [email protected]
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Abstract

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.

Keywords

fairnessmachine learningalgorithmic biasdiscriminationmedical practice
Type
Departments and Columns
Information
Cambridge Quarterly of Healthcare Ethics , Volume 31 , Issue 1 , January 2022 , pp. 83 - 94
Copyright
© The Author(s), 2022. Published by Cambridge University Press

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Footnotes

This section features original work on ethical, legal, policy and social aspects of the use of computing and information technology in health, biomedical research and the health professions. For submissions, contact Kenneth Goodman at: [email protected].

References

Notes

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30. Thanks to an anonymous reviewer for pointing this out.

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32. Cf. See note 25, Barocas, Selbst 2016

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36. The complexity of machine learning models raises wider epistemic and ethical concerns with respect to the noninterpretability of algorithmic decisions. We will not address these issues in this paper. Sullivan, E. Understanding from machine learning models. British Journal for the Philosophy of Science 2020 (online first) gives a good introduction to the epistemic side of the topic.

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43. See note 38, Broome 1998, at 959.

44. See note 35, Broome 2004.

45. Note that this is a simplification. Although claims to medical resources are grounded in medical need, the strength of claims may vary in accordance with other factors such as age. For example, in the current Covid-19 pandemic, an older and a younger person may have the same medical need, in the sense that both have the same probability of survival if put on a ventilator. But it might nevertheless be argued that the younger person has a weaker claim to the resource than the older patient. We are grateful to an anonymous reviewer for pressing us on this point.