AI and Bias in Recruitment: Ensuring Fairness in Algorithmic Hiring
Citation. Ahuchogu, M. C., Musa, G. F. A., Howard, E., & Mathur, K. (2025). AI and Bias in Recruitment: Ensuring Fairness in Algorithmic Hiring. Journal of Informatics Education and Research, 5(3). https://doi.org/10.52783/jier.v5i3.3262
Summary. This narrative article reviews risks that historical data, feature choices, and model design can reproduce inequity in algorithmic hiring. It advocates representative data, explainability, regular audits, regulatory attention, and meaningful human oversight.
Key claims
- Recruitment models trained on historical hiring data can reproduce inequities embedded in prior organizational decisions.
- Fairness in algorithmic hiring requires attention to data, model design, evaluation, and governance rather than treating technical mitigation as sufficient on its own.
- The article identifies audits, explainability, diverse data, and human review as practical safeguards for accountable hiring systems.
Evidence & limitations
- The source is a narrative review and proposal, not a new empirical evaluation of a hiring system or a validation study of a mitigation intervention.
- The downloaded artifact is a Jina full-text rendering; public journal provenance was verified through the DOI landing page.