arXiv cs.CY· Nithin Raghava Ramachandra Narla·· 9 天前AI 评分24
后处理公平性约束何时有效何时有害：来自八项跨域评估的证据
When Post-Processing Fairness Constraints Help and When They Harm: Evidence from Eight Cross-Domain Evaluations
AI 导读
研究提出 FAPE 框架，在刑事司法、收入预测、法律招生、信贷、农业贷款、多域基准、医疗和教育八个领域评估 Fairlearn 的 ThresholdOptimizer 后处理干预。
正文 · 原文
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 base, and interventions validated on one dataset are rarely tested across the heterogeneous domains an organization deploys. We present FAPE (Fairness Auditing for Production Environments), a four-stage framework evaluating a single post-processing intervention, Fairlearn's ThresholdOptimizer, across eight domain evaluations: criminal justice, income prediction, legal admissions, credit lending, agricultural lending, a multi-domain benchmark corpus, healthcare, and education. Each is scored on demographic parity and equalized odds difference, plus disparate impact ratio and accuracy cost where computable. Intervention effectiveness tracks baseline disparity magnitude: across model-domain pairs the constraint improved disparity in 9 of 14 high-disparity cases and worsened it in 3 of 4 near-fair ones. Each of the five high-disparity exceptions reverses under one of two measurement checks, a minimum group size or thresholds fit on held-out data. A CUSUM monitor started at deployment, tested on a simulated shift, separates constrained models that never met a 0.1 parity convention from those that met it and later regressed. A single deployment-time audit is therefore an unreliable guide, which argues for baseline-disparity screening and continuous monitoring
| Comments: | 18 pages, 6 figures, 2 tables. Code, data loaders and figures: this http URL |
| Subjects: | Machine Learning (cs.LG); Computers and Society (cs.CY) |
| Cite as: | arXiv:2609.26955 [cs.LG] |
| (or arXiv:2609.26955v3 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2609.26955 arXiv-issued DOI via DataCite |
Submission history
From: Nithin Raghava Ramachandra Narla [view email]
[v1]
Tue, 22 Sep 2026 18:46:00 UTC (1,657 KB)
[v2]
Tue, 29 Sep 2026 07:09:28 UTC (1,657 KB)
[v3]
Wed, 7 Oct 2026 21:33:20 UTC (1,647 KB)
来源：arXiv cs.CY · arxiv.org