TITLE:
Rejected by Code: Algorithmic Bias in AI-Driven Recruitment and the Case for HR-IT Governance Frameworks
AUTHORS:
Sowmik Sengupta
KEYWORDS:
Algorithmic Bias, AI-Driven Recruitment, Applicant Tracking Systems, Disparate Impact, HR-IT Governance, Vendor Liability
JOURNAL NAME:
World Journal of Engineering and Technology,
Vol.14 No.4,
October
19,
2026
ABSTRACT: Organizations increasingly delegate the first evaluation of job candidates to artificial intelligence, adopted on a dual promise of efficiency and freedom from human prejudice. Scholarship has shown the second promise to be contested: models trained on historical hiring data reproduce past discrimination through facially neutral proxy variables rather than through explicit reference to protected characteristics. Legal scholarship has clarified who may be held liable, and technical scholarship has proposed how such systems might be audited, but the organizational seam between the human resources function that procures a hiring tool and the information technology function that deploys it remains less developed. This paper argues that Mobley v. Workday, Inc. exposes that seam. Drawing on a close reading of the litigation alongside scholarship on algorithmic bias, employment law, and AI governance, it advances three propositions: that AI recruitment tools encode and amplify historical bias through proxy variables; that the Mobley rulings mark a legal inflection point extending potential liability from the employer to the technology vendor; and that managing this exposure requires a joint HR-IT governance framework in which neither function can discharge its responsibility by deferring to the other, to the vendor, or to the algorithm. The paper proposes such a framework, assigning accountable ownership and explicit decision rights across procurement, configuration, monitoring, and review.