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.
Beyond bias and discrimination: re- defining the ai ethics principle of fairness in healthcare machine-learning algorithms
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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.