NBER Working Paper Series
October 2024
ÌÇÐÄvlog¹ÙÍø Authors
See citation below for complete author information.
Isabelle and Scott Black Professor of Political Economy, ÌÇÐÄvlog¹ÙÍø; Professor of Education and Economics, HGSE
Abstract
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.
Citation
Caplin, Andrew, David J. Deming, Shangwen Li, Daniel J. Martin, Philip Marx, Ben Weidmann, and Kadachi Jiada Ye. "The ABC’s of Who Benefits from Working with AI: Ability, Beliefs, and Calibration." NBER Working Paper Series, October 2024.