Do job seekers value procedure in AI hiring only for error correction? Evidence from a conjoint experiment

Research from arXiv explores job seekers' perceptions of AI-driven hiring systems, specifically investigating whether procedural fairness elements are valued independently or primarily for their role in error correction. A conjoint experiment with 1,919 US job seekers found that certain procedural safeguards like appeal, opt-out, and independent bias audits were not valued as expected, suggesting their perceived worth might be tied more to performance and error mitigation rather than inherent procedural value.

This research is strategically important because it informs the design and deployment of AI in human resources, particularly in talent acquisition. Understanding job seeker perceptions is crucial for maintaining public trust, ensuring fair hiring practices, and optimizing the effectiveness and acceptance of automated decision-making systems across various industries.

Employers are increasingly using automated systems for initial job applicant screening, often rejecting candidates without human review.

Forward consideration, not a verified fact.