ProtoFair introduces a fairness-aware contrastive loss that uses unsupervised prototype clustering to create pseudo-counterfactual pairs, encouraging representations invariant to sensitive attributes while integrating with standard SSL frameworks.
In: Larochelle, H., 16 Halawa et al
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ProtoFair: Fair Self-Supervised Contrastive Learning via Pseudo-Counterfactual Pairs
ProtoFair introduces a fairness-aware contrastive loss that uses unsupervised prototype clustering to create pseudo-counterfactual pairs, encouraging representations invariant to sensitive attributes while integrating with standard SSL frameworks.