When Post-Processing Fairness Constraints Help and When They Harm: Evidence from Eight Cross-Domain Evaluations

arXiv:2609.26955 · cs.LG, cs.CY · Submitted 2026-09-22 · Read on arXiv

cs.LG, cs.CY

Submitted: 2026-09-22

Updated: 2026-09-22

Code: https://github.com/nithinnarla/fape-fairness-ml

Terminology

• Accuracy — 31× in this paper · explained in 1 episode
• AUC — 7× in this paper · explained in 2 episodes
• Benchmark — 7× in this paper · explained in 2 episodes
• Trace — 4× in this paper · explained in 2 episodes
• Accountability — 1× in this paper · explained in 2 episodes
• Calibration — 1× in this paper · explained in 7 episodes
• Fairness Auditing for Production Environments — 1× in this paper
• Feature Selection — 1× in this paper · explained in 4 episodes
• Generalizability — 1× in this paper · explained in 6 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• Verification — 1× in this paper · explained in 2 episodes

Sources

• Auditing Fairness under Model Updates: Fundamental Complexity and Property-Preserving Updates
• Bias Begins with Data: The FairGround Corpus for Robust and Reproducible Research on Algorithmic Fairness

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