Study: AI hiring models fail across firms
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A study based on an AI hiring platform used by large low-skill employers found that the retention prediction models the platform trained for each company were highly accurate for that company but only at chance level for other companies. Based on this, the study constructed a company-application matrix and found that the same application submitted to different companies showed highly heterogeneous improvements in retention probability, arising from two different mechanisms. The study also found that even within the same low-skill market, companies disagree on the value of key traits. The study was submitted by Hossein Alidaee et al., with evidence drawn from the aforementioned AI hiring platform.
AI overview based on source reports · Updated 2 days ago
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- Hacker News AILearning about Markets from Models: Evidence from an AI Hiring Platform
Using an AI hiring platform that builds firm-specific models from a standardized psychometric assessment, the authors show each firm's retention model is highly predictive for itself but no better than chance at other firms. Scoring every application against every firm reveals highly heterogeneous retention gains from submitting to a different firm, driven by two distinct mechanisms, and firms disagree about whether important traits improve or worsen retention at the margin. The authors argue these insights require a platform with access to both the full application pool and each firm-specific model, and are valuable to firms, policymakers, and platforms.
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