arXiv Machine Learning August 19, 2026 By Nick Souligne, Vignesh Subbian

Advancing Health Equity through Multi-Level Fairness in Health Informatics

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The paper examines how multi‑level fairness techniques—combining several bias‑mitigation steps—can reduce biases across patient demographics in health informatics. It reviews current literature, identifies gaps in implementation and reporting of health equity outcomes, and evaluates the role of reporting standards such as MINIMAR and TRIPOD in enhancing transparency. The authors conclude with recommendations to improve reporting transparency, broaden adoption of multi‑level fairness methods, and explicitly prioritize health equity in future research.

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