Can Labor Markets Function in the Age of AI? The Evaluation Bottleneck in Hiring
AI-assisted job-search tools have become increasingly popular by making it easier to find and apply to jobs. But by making it easier for applicants to generate and tailor application materials, they can also reduce how informative those materials are about applicant fit. We study this tradeoff in a hiring market where applicants differ in experience and latent match quality and firms use noisy application materials to decide whom to screen. We ask how AI affects downstream screening and hiring, and which applicants are most adversely affected. As application materials become less informative, a Bayesian firm rationally relies more heavily on coarse observables such as prior experience. Am...
| Authors | Itai Ashlagi, Ramesh Johari, Jon Kleinberg et al. |
|---|---|
| Primary category | cs.GT |
| Published | 24 September 2026 |
| Last updated at source | 24 September 2026 |
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