arXiv cs.CY· Warren Jouanneau, Morgane Hoffmann, Emma Jouffroy, Marc Palyart·· 11 小时前AI 评分32
面向招聘检索效果与公平性评估的分类驱动合成数据集
A Taxonomy-Driven Synthetic Dataset for Retrieval Effectiveness and Fairness Evaluation in Hiring
AI 导读
研究者构建了一个基于分类体系的全合成 HR 数据集与基准，可同时评估检索效果与算法公平性，语料以 660K 真实自由职业者档案为基础，包含候选人档案、项目简介和反事实人口统计孪生样本，并支持多语言。
正文 · 原文
Abstract:While machine learning hiring tools are ubiquitous, the need for privacy regulations and commercial sensitivity makes public benchmarking legally complex. We address this challenge by introducing a taxonomy-driven, fully synthetic HR dataset and benchmark to simultaneously evaluate retrieval effectiveness and algorithmic fairness. Grounded in 660K real freelancer profiles, our multilingual corpus features candidate profiles, project briefs, and counterfactual demographic twins. Auditing state-of-the-art embedding models reveals performance divergence from general MTEB scores, alongside fairness disparities in grammatically gendered languages. This demonstrates that general benchmarks are insufficient for HR, requiring tailored evaluations to identify Pareto-optimal models that balance semantic accuracy and fairness.
| Subjects: | Machine Learning (cs.LG); Computation and Language (cs.CL); Computers and Society (cs.CY); Information Retrieval (cs.IR) |
| Cite as: | arXiv:2611.09178 [cs.LG] |
| (or arXiv:2611.09178v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2611.09178 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Warren Jouanneau Mr [view email]
[v1]
Fri, 6 Nov 2026 08:59:52 UTC (3,535 KB)
来源：arXiv cs.CY · arxiv.org