← 研踪 PaperTrailarXiv:2610.36649 · v1
When Is a Cheap Fairness Edit Safe? Screening Post-Hoc Edits in Two-Sided Matching Markets
首次提交 2026-10-27 13:07
版本更新 2026-10-27 13:07
雷达观测 2026-10-29 00:35
摘要
Two-sided matching systems, such as admissions pipelines, allocate opportunities jointly. Reducing a protected at tribute's influence usually requires encoder retraining. We study a closed-form linear projector that does this at inference time. For markets that stay vertically ordered before and after the edit, we bound the mean welfare change by the edit-induced appeal shift and an explicit remainder. A probabilistic refinement replaces worst-case constants with a data-dependent deviation term. A pre-deployment screen combines a verticality test with a welfare sweep and returns PASS, KILL, or OUT-OF-SCOPE. An exact tier requires every opportunity to rank applicants by appeal, and a graded tier admits near-vertical markets. On 30 held-out synthetic markets, the screen agrees with every pre-specified regime verdict, with no observed false PASS. On 60 exactly vertical markets, no bound is violated, and the probabilistic bound is informative in 69% to 87% of configurations. On 2700 configurations from three realistic markets, which do not reach the exact tier, no plug-in bound expression is violated. A closed-form factorization stays within graded scope in 90% to 100% of instances, whereas two-tower and graph neural network encoders fall outside it in every instance. Architecture choice thus appears to affect whether a post-hoc edit is admissible.
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v1 · 2026-10-27 13:07 · 最新观测版本
When Is a Cheap Fairness Edit Safe? Screening Post-Hoc Edits in Two-Sided Matching Markets
Two-sided matching systems, such as admissions pipelines, allocate opportunities jointly. Reducing a protected at tribute's influence usually requires encoder retraining. We study a closed-form linear projector that does this at inference time. For markets that stay vertically ordered before and after the edit, we bound the mean welfare change by the edit-induced appeal shift and an explicit remainder. A probabilistic refinement replaces worst-case constants with a data-dependent deviation term. A pre-deployment screen combines a verticality test with a welfare sweep and returns PASS, KILL, or OUT-OF-SCOPE. An exact tier requires every opportunity to rank applicants by appeal, and a graded tier admits near-vertical markets. On 30 held-out synthetic markets, the screen agrees with every pre-specified regime verdict, with no observed false PASS. On 60 exactly vertical markets, no bound is violated, and the probabilistic bound is informative in 69% to 87% of configurations. On 2700 configurations from three realistic markets, which do not reach the exact tier, no plug-in bound expression is violated. A closed-form factorization stays within graded scope in 90% to 100% of instances, whereas two-tower and graph neural network encoders fall outside it in every instance. Architecture choice thus appears to affect whether a post-hoc edit is admissible.
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