The ABCs of Who Benefits from Working with AI: Ability, Beliefs, and Calibration

We use a controlled experiment to show that ability and belief calibration jointly determine the benefits of working with artificial intelligence (AI). AI improves performance more for people with low baseline ability. However, holding ability constant, AI assistance is more valuable for people who are calibrated, meaning they have accurate beliefs about their own ability. People who know they have low ability gain the most from working with AI. In a counterfactual analysis, we show that eliminating miscalibration would cause AI to reduce performance inequality nearly twice as much as it already does. This paper was accepted by Marie Claire Villeval, behavioral economics and decision analysis. Funding: This work was supported by the Alfred P. Sloan Foundation (Cognitive Economics at Work). Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2024.08994 .

• 저널Management Science
• 토픽Explainable Artificial Intelligence (XAI) · Artificial Intelligence
• DOI10.1287/mnsc.2024.08994
• 저자Andrew Caplin, David Deming, Shangwen Li, Daniel Martin, Philip Marx, Ben Weidmann, K. Ye