PopResume: Causal Fairness Evaluation of LLM/VLM Resume Screeners with Population-Representative Dataset

arXiv:2603.22714 · cs.CY, cs.AI · Submitted 2026-03-24 · Read on arXiv

cs.CY, cs.AI

Submitted: 2026-03-24

Updated: 2026-09-08

Comments: EMNLP 2026 Main

Code: https://github.com/srbhr/Resume-Matcher

License: http://creativecommons.org/licenses/by/4.0/

The gist: We present PopResume, a population-representative resume dataset for causal fairness auditing of LLM- and VLM-based resume screening systems.

Terminology

Abstract

We present PopResume, a population-representative resume dataset for causal fairness auditing of LLM- and VLM-based resume screening systems. Unlike existing benchmarks that rely on manually injected demographic information and outcome-level disparities, PopResume is grounded in population statistics and preserves natural attribute relationships, enabling path-specific effect (PSE)-based fairness evaluation. We decompose the effect of a protected attribute on resume scores into two paths: the business necessity path, mediated by job-relevant qualifications, and the redlining path, mediated by demographic proxies. This distinction allows auditors to separate legally permissible from impermissible sources of disparity. Evaluating four LLMs and four VLMs on PopResume's 60.8K resumes across five occupations, we identify five representative discrimination patterns that aggregate metrics fail to capture. Our results demonstrate that PSE-based evaluation reveals fairness issues masked by outcome-level measures, underscoring the need for causally-grounded auditing frameworks in AI-assisted hiring.

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