Balancing Fairness and Performance in Healthcare AI: A Gradient Reconciliation Approach

• URL: http://arxiv.org/abs/2504.14388v1
• Date: Sat, 19 Apr 2025 19:24:34 GMT
• Title: Balancing Fairness and Performance in Healthcare AI: A Gradient Reconciliation Approach
• Authors: Xiaoyang Wang, Christopher C. Yang,
• Abstract summary: AI systems deployed without explicit fairness considerations risk exacerbating existing healthcare disparities.<n>We propose FairGrad, a novel gradient reconciliation framework that balances predictive performance and multi-attribute fairness optimization.
• Score: 3.997371369137763
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The rapid growth of healthcare data and advances in computational power have accelerated the adoption of artificial intelligence (AI) in medicine. However, AI systems deployed without explicit fairness considerations risk exacerbating existing healthcare disparities, potentially leading to inequitable resource allocation and diagnostic disparities across demographic subgroups. To address this challenge, we propose FairGrad, a novel gradient reconciliation framework that automatically balances predictive performance and multi-attribute fairness optimization in healthcare AI models. Our method resolves conflicting optimization objectives by projecting each gradient vector onto the orthogonal plane of the others, thereby regularizing the optimization trajectory to ensure equitable consideration of all objectives. Evaluated on diverse real-world healthcare datasets and predictive tasks - including Substance Use Disorder (SUD) treatment and sepsis mortality - FairGrad achieved statistically significant improvements in multi-attribute fairness metrics (e.g., equalized odds) while maintaining competitive predictive accuracy. These results demonstrate the viability of harmonizing fairness and utility in mission-critical medical AI applications.

Related papers

• Fair Foundation Models for Medical Image Analysis: Challenges and Perspectives [2.5573554033525636]
  Foundation Models (FMs), trained on vast datasets through self-supervised learning, enable efficient adaptation across medical imaging tasks.<n>These models demonstrate potential for enhancing fairness, though significant challenges remain in achieving consistent performance across demographic groups.<n>This comprehensive framework advances current knowledge by demonstrating how systematic bias mitigation, combined with policy engagement, can effectively address both technical and institutional barriers to equitable AI in healthcare.
  arXiv  Detail & Related papers  (2025-02-24T04:54:49Z)
• Enhancing Multi-Attribute Fairness in Healthcare Predictive Modeling [3.997371369137763]
  We introduce a novel approach to multi-attribute fairness optimization in healthcare AI, tackling fairness concerns across multiple demographic attributes concurrently.<n>Our results show a significant reduction in Equalized Odds Disparity (EOD) for multiple attributes, while maintaining high predictive accuracy.
  arXiv  Detail & Related papers  (2025-01-22T21:02:08Z)
• AI-Driven Healthcare: A Survey on Ensuring Fairness and Mitigating Bias [2.398440840890111]
  AI applications have significantly improved diagnostic accuracy, treatment personalization, and patient outcome predictions. These advancements also introduce substantial ethical and fairness challenges. These biases can lead to disparities in healthcare delivery, affecting diagnostic accuracy and treatment outcomes across different demographic groups.
  arXiv  Detail & Related papers  (2024-07-29T02:39:17Z)
• A Conceptual Algorithm for Applying Ethical Principles of AI to Medical Practice [5.005928809654619]
  AI-powered tools are increasingly matching or exceeding specialist-level performance across multiple domains.<n>These systems promise to reduce disparities in care delivery across demographic, racial, and socioeconomic boundaries.<n>The democratization of such AI tools can reduce the cost of care, optimize resource allocation, and improve the quality of care.
  arXiv  Detail & Related papers  (2023-04-23T04:14:18Z)
• Stochastic Methods for AUC Optimization subject to AUC-based Fairness Constraints [51.12047280149546]
  A direct approach for obtaining a fair predictive model is to train the model through optimizing its prediction performance subject to fairness constraints. We formulate the training problem of a fairness-aware machine learning model as an AUC optimization problem subject to a class of AUC-based fairness constraints. We demonstrate the effectiveness of our approach on real-world data under different fairness metrics.
  arXiv  Detail & Related papers  (2022-12-23T22:29:08Z)
• Fair Machine Learning in Healthcare: A Review [90.22219142430146]
  We analyze the intersection of fairness in machine learning and healthcare disparities. We provide a critical review of the associated fairness metrics from a machine learning standpoint. We propose several new research directions that hold promise for developing ethical and equitable ML applications in healthcare.
  arXiv  Detail & Related papers  (2022-06-29T04:32:10Z)
• Robust and Efficient Medical Imaging with Self-Supervision [80.62711706785834]
  We present REMEDIS, a unified representation learning strategy to improve robustness and data-efficiency of medical imaging AI. We study a diverse range of medical imaging tasks and simulate three realistic application scenarios using retrospective data.
  arXiv  Detail & Related papers  (2022-05-19T17:34:18Z)
• Algorithm Fairness in AI for Medicine and Healthcare [4.626801344708786]
  algorithm fairness is a challenging problem in delivering equitable care. Recent evaluation of AI models stratified across race sub-populations have revealed enormous inequalities in how patients are diagnosed, given treatments, and billed for healthcare costs.
  arXiv  Detail & Related papers  (2021-10-01T18:18:13Z)
• Bootstrapping Your Own Positive Sample: Contrastive Learning With Electronic Health Record Data [62.29031007761901]
  This paper proposes a novel contrastive regularized clinical classification model. We introduce two unique positive sampling strategies specifically tailored for EHR data. Our framework yields highly competitive experimental results in predicting the mortality risk on real-world COVID-19 EHR data.
  arXiv  Detail & Related papers  (2021-04-07T06:02:04Z)
• UNITE: Uncertainty-based Health Risk Prediction Leveraging Multi-sourced Data [81.00385374948125]
  We present UNcertaInTy-based hEalth risk prediction (UNITE) model. UNITE provides accurate disease risk prediction and uncertainty estimation leveraging multi-sourced health data. We evaluate UNITE on real-world disease risk prediction tasks: nonalcoholic fatty liver disease (NASH) and Alzheimer's disease (AD) UNITE achieves up to 0.841 in F1 score for AD detection, up to 0.609 in PR-AUC for NASH detection, and outperforms various state-of-the-art baselines by up to $19%$ over the best baseline.
  arXiv  Detail & Related papers  (2020-10-22T02:28:11Z)
• Causal Feature Selection for Algorithmic Fairness [61.767399505764736]
  We consider fairness in the integration component of data management. We propose an approach to identify a sub-collection of features that ensure the fairness of the dataset.
  arXiv  Detail & Related papers  (2020-06-10T20:20:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.