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

arXiv:2609.16390v1 Announce Type: new Abstract: Employers increasingly delegate initial screening to automated systems, which in many cases reject an application before any human reads it. Acceptance of such systems plausibly depends both on how well they perform and on the procedure that produces the decision. Prior studies rarely vary procedure and performance independently, leaving it unclear whether applicants value procedure for its own sake or for the errors it corrects. In a preregistered paired-profile conjoint experiment, 1,919 United States job seekers made eight choices between systems with independently randomized levels of decision authority, error rate, explanation, opt-out, appeal, and independent bias audit. The value of the appeal, the opt-out, and the bias audit did not rise as wrongful rejections became more common, each staying within a preregistered equivalence bound. Human involvement carried more weight than any procedural feature, moving stated choice about as much as cutting wrongful rejections from 30% to 10%. These patterns constrain a simple error-correction account and are consistent with applicants valuing procedure partly for its own sake, so that improving a system's performance does not substitute for a right applicants can invoke.

AI hiring systems may devalue human involvement and perpetuate biases

The study shows job seekers value procedural features in AI hiring beyond error correction, highlighting the importance of transparency and fairness. While this suggests potential benefits in perceived legitimacy, the high error rates and reliance on humans indicate current AI hiring systems may still harm applicants by wrongful rejections. Overall impact is cautiously positive but depends on implementation.

The study finds that job seekers value procedural features in AI hiring systems independently of error rates. This suggests that improving procedural fairness can increase trust and acceptance of AI in hiring, which is positive for human flourishing. Human involvement is also valued highly, indicating a need for balanced AI-human systems.

SOURCE     arXiv cs.CY (Computers and Society) (tier 1)
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DOCUMENT   3996a87c-0493-45c0-a459-07efea4b5e2a
           https://arxiv.org/abs/2609.16390
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EVIDENCE   3 extracted excerpts
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MODEL RUN  4 runs, methodology 1.0.1
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SCORE      +2  (Mixed or uncertain)
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CONFIDENCE 76%