SOURCE-LINKED INTELLIGENCE
Counterfactual Bias Testing for Application Tracking System
Automated candidate-job matching systems are increasingly classified as high-risk AI under emerging regulation, yet auditing them for demographic bias is expensive: classical correspondence-audit studies require hand-crafted resumes and manual submission, which does not scale to fast pipeline retraining cycles. This paper presents a general, reusable methodology that (1) uses task-specialized LLM agents to synthesize identity-neutral base resumes and inject controlled demographic treatments across five protected-characteristic axes (sex/gender, age, residence, language, disability), producing
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-08-27T10:01:36.000Z
First collected: 2026-09-21T08:51:59.673Z. This is not the publication date.