FairMed-XGB: A Bayesian-Optimised Multi-Metric Framework with Explainability for Demographic Equity in Critical Healthcare Data
Machine learning models deployed in critical care settings exhibit demographic biases, particularly gender disparities, that undermine clinical trust and equitable treatment. This paper introduces FairMed-XGB, a novel framework that systematically detects and mitigates gender-based prediction bias while preserving model performance and transparency. The framework integrates a fairness-aware loss function combining Statistical Parity Difference, Theil Index, and Wasserstein Distance, jointly opti
Record details
Published: 16 March 2026
Source: arXiv
Category: Research
Topics: Bias & fairness · Healthcare · Transparency
Retrieved: 14 July 2026
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How to cite this record
ethics.ai (16 March 2026), “FairMed-XGB: A Bayesian-Optimised Multi-Metric Framework with Explainability for Demographic Equity in Critical Healthcare Data,” evidence record 7191, https://ethics.ai/record/7191 (originally published by arXiv).
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