Enabling Fairness in Healthcare Through Machine Learning

Ethics and Information Technology 24 (3):1-13 (2022)
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Abstract

The use of machine learning systems for decision-support in healthcare may exacerbate health inequalities. However, recent work suggests that algorithms trained on sufficiently diverse datasets could in principle combat health inequalities. One concern about these algorithms is that their performance for patients in traditionally disadvantaged groups exceeds their performance for patients in traditionally advantaged groups. This renders the algorithmic decisions unfair relative to the standard fairness metrics in machine learning. In this paper, we defend the permissible use of affirmative algorithms; that is, algorithms trained on diverse datasets that perform better for traditionally disadvantaged groups. Whilst such algorithmic decisions may be unfair, the fairness of algorithmic decisions is not the appropriate locus of moral evaluation. What matters is the fairness of final decisions, such as diagnoses, resulting from collaboration between clinicians and algorithms. We argue that affirmative algorithms can permissibly be deployed provided the resultant final decisions are fair.

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References found in this work

On statistical criteria of algorithmic fairness.Brian Hedden - 2021 - Philosophy and Public Affairs 49 (2):209-231.
Responsibility for implicit bias.Jules Holroyd - 2017 - Philosophy Compass 12 (3).
Transparency in Complex Computational Systems.Kathleen A. Creel - 2020 - Philosophy of Science 87 (4):568-589.
Responsibility for Implicit Bias.Jules Holroyd - 2012 - Journal of Social Philosophy 43 (3):274-306.

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