AI Recruitment Bias Governance through Multi-Case Comparative Study: From the Infeasibility of “Zero Bias” to Auditable Compliance and Engineering Practices

DOI

10.2991/978-94-6239-699-9_3How to use a DOI?

Keywords

AI recruitment; algorithmic bias; FATE framework; fairness governance; third-party audit; human-AI collaboration; ethics and compliance

Abstract

Artificial intelligence is revolutionizing recruitment, with 83% of employers using automated screening systems [14]. While boosting efficiency, AI introduces algorithmic bias and transparency issues, as seen in cases involving Amazon and HireVue [14]. Through case studies of Harver, Eightfold AI, HireVue, and LinkedIn, this study finds bias stems from the interaction of data, algorithms, and human interpretation [2;12]. Bias in AI recruitment systems does not arise from isolated technical flaws but from a structural coupling between social inequality and computational optimization. Historical labor market inequalities shape training data distributions; these distributions are then formalized through algorithmic objective functions (e.g., predictive accuracy or retention likelihood), which systematically privilege historically dominant groups. I propose the Auditable Fairness Framework (AFF)—based on Auditability, Engineering, Control, and Remediation—shifting the goal from unachievable “zero bias” to establishing detectable, explainable, and correctable governance.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Zihe Qi
PY  - 2026
DA  - 2026/06/02
TI  - AI Recruitment Bias Governance through Multi-Case Comparative Study: From the Infeasibility of “Zero Bias” to Auditable Compliance and Engineering Practices
BT  - Proceedings of the 2026 4th International Conference on Digital Economy and Management Science (CDEMS 2026)
PB  - Atlantis Press
SP  - 12
EP  - 19
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6239-699-9_3
DO  - 10.2991/978-94-6239-699-9_3
ID  - Qi2026
ER  -