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

Zihe Qi ()
Additional contact information
Zihe Qi: University of Illinois Urbana-Champaign, Laber and Industrial Relations

A chapter in Proceedings of the 2026 4th International Conference on Digital Economy and Management Science (CDEMS 2026), 2026, pp 12-19 from Springer

Abstract: 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.

Keywords: AI recruitment; algorithmic bias; FATE framework; fairness governance; third-party audit; human-AI collaboration; ethics and compliance (search for similar items in EconPapers)
Date: 2026
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:advbcp:978-94-6239-699-9_3

Ordering information: This item can be ordered from
http://www.springer.com/9789462396999

DOI: 10.2991/978-94-6239-699-9_3

Access Statistics for this chapter

More chapters in Advances in Economics, Business and Management Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().