AINeutralarXiv – CS AI · Apr 106/10
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CAFP: A Post-Processing Framework for Group Fairness via Counterfactual Model Averaging
Researchers introduce CAFP, a post-processing framework that mitigates algorithmic bias by averaging predictions across factual and counterfactual versions of inputs where sensitive attributes are flipped. The model-agnostic approach eliminates the need for retraining or architectural modifications, making fairness interventions practical for deployed systems in high-stakes domains like credit scoring and criminal justice.
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