Behavioral Measures Improve AI Hiring: A Field Experiment

Marie-Pierre Dargnies, Rustamdjan Hakimov and Dorothea Kübler
Additional contact information
Marie-Pierre Dargnies: University of Paris Dauphine
Rustamdjan Hakimov: University of Lausanne
Dorothea Kübler: WZB Berlin, Technische Universität Berlin, CES Ifo

No 532, Rationality and Competition Discussion Paper Series from CRC TRR 190 Rationality and Competition

Abstract: The adoption of Artificial Intelligence (AI) for hiring processes is often impeded by a scarcity of comprehensive employee data. We hypothesize that the inclusion of behavioral measures elicited from applicants can enhance the predictive accuracy of AI in hiring. We study this hypothesis in the context of microfinance loan officers. Our findings suggest that survey-based behavioral measures markedly improve the predictions of a random-forest algorithm trained to predict productivity within sample relative to demographic information alone. We then validate the algorithm’s robustness to the selectivity of the training sample and potential strategic responses by applicants by running two out-of-sample tests: one forecasting the future performance of novice employees, and another with a field experiment on hiring. Both tests corroborate the effectiveness of incorporating behavioral data to predict performance. The comparison of workers hired by the algorithm with those hired by human managers in the field experiment reveals that algorithmic hiring is marginally more efficient than managerial hiring.

Keywords: hiring; ai; economic and behavioral measures; selective labels (search for similar items in EconPapers)
Date: 2025-04-29
New Economics Papers: this item is included in nep-ain, nep-exp, nep-for and nep-hrm
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://rationality-and-competition.de/wp-content/ ... ussion_paper/532.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:rco:dpaper:532

Access Statistics for this paper

More papers in Rationality and Competition Discussion Paper Series from CRC TRR 190 Rationality and Competition
Bibliographic data for series maintained by Viviana Lalli ().