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

• 成果类型: Article
• 署名作者: Caplin, Andrew; Deming, David; Li, Shangwen; Martin, Daniel; Marx, Philip; Weidmann, Ben; Ye, Kadachi Jiada
• 署名单位: New York University; Harvard University; University of California System; University of California Santa Barbara; Louisiana State University System; Louisiana State University
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2024.08994
• 发表日期: 2026
• 关键词: Artificial intelligence (AI); human-AI collaboration; algorithmic advice; labor market inequality; skills; beliefs; calibration; overconfidence

摘要

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.