bias mitigation

Fix Fairness, Don't Ruin Accuracy: Performance Aware Fairness Repair using AutoML
Our approach includes two key innovations: a novel optimization function and a fairness-aware search space. By improving the default optimization function of AutoML and incorporating fairness objectives, we are able to mitigate bias with little to no loss of accuracy. Additionally, we propose a fairness-aware search space pruning method for AutoML to reduce computational cost and repair time.
Giang Nguyen, Sumon Biswas, Hridesh Rajan
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