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Publication (Zenodo)
Published on Zenodo
15 June 2024
Mitigating Bias in AI-Powered Recruitment
AI-powered hiring tools can amplify historical bias unless fairness is engineered into the release process. This paper outlines evaluation frameworks for candidate ranking — demographic parity checks, explainability requirements, and recruiter override loops — drawn from production ATS work at Noble House Consulting.
Key takeaways
- •Bias mitigation is a release gate, not a one-time audit.
- •Explainable ranking signals improve recruiter trust and catch drift early.
- •Override reasons from recruiters should feed back into evaluation datasets.
- •Holdout sets must reflect real funnel diversity, not lab conditions.
Abstract
Techniques and evaluation frameworks to reduce algorithmic bias in candidate ranking and screening systems.