Screening AI-equipped applicants

Abstract

Generative AI can lower the cost of producing polished application materials without improving applicants' abilities to do the job. We study an employer who observes qualifications and work history, summarized by observable quality, but must infer other job-relevant attributes from reports that applicants can manipulate at costs that depend on privately known manipulation ability. The employer commits to a hiring rule under a capacity constraint, and we evaluate rules by match value net of applicants' manipulation costs. We show that, as manipulation ability becomes increasingly dispersed among applicants with the same observable quality, socially optimal hiring converges to selection on observable quality alone, a phenomenon we call strategic feature collapse. To capture the remaining welfare gain from reports, we construct a simple dual-track mechanism that directly admits applicants with high observable quality and offers some otherwise excluded applicants a report-based route to admission. Despite the complexity of multidimensional screening, this mechanism is asymptotically optimal under additional regularity, matching the unrestricted optimum's welfare gain from reports through second order.