Advancing Health Equity through Multi-Level Fairness in Health Informatics
The increasing integration of machine learning in healthcare has highlighted critical challenges related to fairness, transparency, and health equity. Specifically, the use of multi-level fairness techniques, which combine multiple bias mitigation steps or techniques, show promise for reducing biases across different patient demographics, yet this approach remains underexplored in terms of its health equity outcomes. In this paper, we assess the current landscape of multi-level fairness in health informatics by focusing on its impact on equitable healthcare outcomes and evaluating how transparency and reporting standards contribute to these advancements. Through an examination of the exis...
| Authors | Nick Souligne, Vignesh Subbian |
|---|---|
| Primary category | cs.CY |
| Published | 9 July 2026 |
| Last updated at source | 9 July 2026 |
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