The ABC’s of Who Benefits from Working with AI: Ability, Beliefs, and Calibration
校准
心理学
计算机科学
社会心理学
统计
数学
作者
Andrew Caplin,David Deming,Shangwen Li,Daniel Martín,Philip Marx,Ben Weidmann,K. Ye
标识
DOI:10.3386/w33021
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.