InfoResearchPeer-reviewed
CIRCUS: A Causal Intervention-Based Framework for Enhancing Counterfactual Fairness in Trained Classifiers
- Published
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Summary
The paper presents CIRCUS, a causal intervention framework for improving counterfactual fairness in trained classifiers, built on a new generator called CITGAN. CIRCUS generates counterfactually discriminatory samples via causal intervention, then applies bias correction to their labels and retrains the classifier. On a DNN, the MMD_L and MMD_K values fell by averages of 39.7% and 40.4% versus the second-best result; for ResNet, the reductions were 56.7% and 54.5%.
Mitigation
The mitigation is the CIRCUS framework itself: generating counterfactually discriminatory samples through causal intervention, applying bias correction preprocessing to their labels, and retraining the classifier.