The paper proposes GESD, a procedural fairness metric for group disparities in explanation stability and robustness, and integrates it into the FEU multi-objective optimization framework.
Optimized pre-processing for discrimination prevention
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cs.LG 2years
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Bilevel optimization learns client weights to defend fairness in one-shot collaborative ML by anchoring to a small trusted root dataset at the server.
citing papers explorer
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GESD: Beyond Outcome-Oriented Fairness
The paper proposes GESD, a procedural fairness metric for group disparities in explanation stability and robustness, and integrates it into the FEU multi-objective optimization framework.
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Robust Server Defense Against Unreliable Clients in One-Shot Fair Collaborative Machine Learning
Bilevel optimization learns client weights to defend fairness in one-shot collaborative ML by anchoring to a small trusted root dataset at the server.