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Algorithmic individual fairness and healthcare: a scoping review
Joshua W Anderson1, Shyam Visweswaran1,2
1Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA 15213, United States.
JAMIA Open
|December 31, 2024
Summary
Algorithmic individual fairness (IF) research is emerging, particularly for healthcare applications. More studies are needed to define and evaluate IF methods to ensure equitable clinical care.
Area of Science:
- Healthcare AI
- Algorithmic Fairness
- Bias Mitigation
Background:
- Artificial intelligence (AI) algorithms in healthcare may perpetuate and amplify existing disparities.
- There is a critical need to understand and mitigate algorithmic biases for equitable clinical care.
- Algorithmic individual fairness (IF) offers a promising approach to address these concerns.
Purpose of the Study:
- To conduct a scoping review of algorithmic individual fairness (IF).
- To understand the current metrics, methods, and healthcare applications of IF.
- To identify research gaps and future directions in IF for healthcare.
Main Methods:
- Scoping review of four databases (PubMed, ACM, IEEE, medRxiv) for IF research.
- Search restricted to articles published between January 2013 and November 2024.
- Synthesis of data from 32 included articles.
Main Results:
- Identified themes: philosophical underpinnings, IF metrics, mitigation methods, group vs. individual fairness implications, and healthcare applications.
- Research on IF is nascent, especially in healthcare, with limited publications.
- Healthcare applications of IF are sparse but show steady growth.
Conclusions:
- Algorithmic IF research, definitions, and applications are in early stages within healthcare.
- Challenges include subjective similarity definitions and potential data-driven bias.
- Further comprehensive research is essential to advance IF in healthcare settings.
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