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Racial Bias in Clinical and Population Health Algorithms: A Critical Review of Current Debates
Madison Coots1, Kristin A Linn2, Sharad Goel1
1Harvard Kennedy School, Harvard University, Cambridge, Massachusetts, USA.
Annual Review of Public Health
|December 3, 2024
Summary
Ensuring fairness in health care algorithms is complex. Popular fairness approaches may worsen outcomes for all racial groups, necessitating a new consequentialist framework focused on equitable decision-making.
Area of Science:
- Health Informatics
- Biomedical Ethics
- Computer Science
Background:
- Debate exists on assessing and ensuring fairness in health care algorithms.
- Potential racial bias in clinical decision support and population health algorithms is a key concern.
Purpose of the Study:
- To categorize concerns regarding health care algorithm fairness.
- To critically examine prominent health care algorithms using this taxonomy.
- To propose an alternative framework for equitable algorithm design.
Main Methods:
- Distilled fairness concerns into four categories: race inclusion/exclusion, unequal decision rates, unequal error rates, and target variable bias.
- Critically examined seven prominent health care algorithms.
Main Results:
- Popular fairness-enhancing approaches can negatively impact outcomes across all racial and ethnic groups.
- Existing methods may inadvertently exacerbate disparities.
Conclusions:
- A consequentialist framework prioritizing outcomes and clarifying trade-offs is proposed.
- This approach aims to mitigate harms and promote equitable decision-making in health care algorithms.
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