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Preprint (Author's original)arXiv.org - Non-exclusive license to distribute, Open
Preprint
Enhancing Multi-Attribute Fairness in Healthcare Predictive Modeling
22 Jan 2025
Abstract
Artificial intelligence (AI) systems in healthcare have demonstrated
remarkable potential to improve patient outcomes. However, if not designed with
fairness in mind, they also carry the risks of perpetuating or exacerbating
existing health disparities. Although numerous fairness-enhancing techniques
have been proposed, most focus on a single sensitive attribute and neglect the
broader impact that optimizing fairness for one attribute may have on the
fairness of other sensitive attributes. In this work, we introduce a novel
approach to multi-attribute fairness optimization in healthcare AI, tackling
fairness concerns across multiple demographic attributes concurrently. Our
method follows a two-phase approach: initially optimizing for predictive
performance, followed by fine-tuning to achieve fairness across multiple
sensitive attributes. We develop our proposed method using two strategies,
sequential and simultaneous. Our results show a significant reduction in
Equalized Odds Disparity (EOD) for multiple attributes, while maintaining high
predictive accuracy. Notably, we demonstrate that single-attribute fairness
methods can inadvertently increase disparities in non-targeted attributes
whereas simultaneous multi-attribute optimization achieves more balanced
fairness improvements across all attributes. These findings highlight the
importance of comprehensive fairness strategies in healthcare AI and offer
promising directions for future research in this critical area.
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Details
- Title
- Enhancing Multi-Attribute Fairness in Healthcare Predictive Modeling
- Creators
- Xiaoyang WangChristopher C Yang
- Resource Type
- Preprint
- Language
- English
- Academic Unit
- Information Science; College of Computing and Informatics
- Other Identifier
- 991022020638904721