Behavioral measures improve AI hiring: A field experiment
The DEMS Economics Seminar series is proud to host
Rustamdjan Hakimov
(University of Lausanne)
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 credit 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. At the same time, our field experiment comparing workers hired by the algorithm with those hired by human managers reveals that algorithms hires better workers, though the treatment effects are marginally significant.
The seminar will be in presence, Seminar Room 2104, Building U7-2nd floor