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 a K x (1+N) correspondence-audit matrix; (2) qualitatively flags inferred protected characteristi...
| Authors | Sai Yashwant, Shruti Bansal, Anurag Dubey et al. |
|---|---|
| Primary category | cs.AI |
| Published | 27 August 2026 |
| Last updated at source | 27 August 2026 |
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