When post-processing fairness constraints help and when they harm: evidence from eight cross-domain evaluations
Read the original at arxiv.org→arXiv:2609.26955v1 Announce Type: new Abstract: Fairness audits in production ML typically occur once, at deployment, on a single domain. Both fail in practice: fairness can shift after retraining or a changing user...
Original headline: "When Post-Processing Fairness Constraints Help and When They Harm: Evidence from Eight Cross-Domain Evaluations"
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- Sep 24, 04:00 UTC arXiv cs.LG lead source When Post-Processing Fairness Constraints Help and When They Harm: Evidence from Eight Cross-Domain Evaluations