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Fairness in AI-Driven Recruitment: Challenges, Metrics, Methods, and Future Directions
Citation. Mujtaba, D. F. & Mahapatra, N. R. (2024). Fairness in AI-Driven Recruitment: Challenges, Metrics, Methods, and Future Directions. arXiv:2405.19699.
Summary. This preprint reviews sources of bias, fairness metrics, mitigation methods, and auditing approaches across AI-enabled recruitment, including advertising, résumé screening, interviewing, and automated assessment. It argues that fairness evaluation must be aligned with the particular hiring task and job requirements rather than reduced to one universal metric. The review identifies gaps in standardized benchmarks, auditing guidance, transparency, and long-term evaluation as hiring models and legal requirements change.
Key claims
- AI recruitment can reproduce or amplify historical and proxy-based discrimination across candidate sourcing, screening, interview assessment, and selection unless the full decision pipeline is examined.
- Fairness metrics impose different trade-offs and may not be simultaneously satisfiable; the review recommends choosing metrics in relation to the recruitment stage, candidate qualifications, and protected-group representation.
- Bias mitigation can be applied through pre-processing, in-processing, or post-processing, but its suitability depends on the source of bias, access to model architecture and data, and whether a vendor system is outsourced.
- Continuous auditing and transparent decision feedback are necessary because hiring models, organizational practices, and relevant legislation change over time.
Evidence & limitations
- Narrative/systematic review of interdisciplinary literature rather than a new empirical study; its recommendations synthesize prior research and practical examples.
- Public arXiv preprint (v3 dated 2025-05-18); the ingested Drive PDF exactly matches the public arXiv PDF by SHA-256, while the source’s stable publication year is 2024.