Behavioral Measures Improve AI Hiring: A Field Experiment

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.