Fair-MoE reports improved accuracy and fairness on Harvard-FairVLMed for some protected attributes by adding sparse mixture-of-experts layers and a variance-based fairness loss to CLIP, but the all-attribute improvement claim is contradicted by its own tables.
How fair are medical imaging foundation models? In Machine Learning for Health (ML4H) , pages 217–231
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Fair-MoE: Fairness-Oriented Mixture of Experts in Vision-Language Models
Fair-MoE reports improved accuracy and fairness on Harvard-FairVLMed for some protected attributes by adding sparse mixture-of-experts layers and a variance-based fairness loss to CLIP, but the all-attribute improvement claim is contradicted by its own tables.