The ABC's of Who Benefits from Working with AI : Ability, Beliefs, and Calibration

Andrew Caplin, David J. Deming, Shangwen Li, Daniel J. Martin, Philip Marx, Ben Weidmann, Kadachi Jiada Ye

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

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Year of publication:	October 2024
Authors:	Caplin, Andrew ; Deming, David J. ; Li, Shangwen ; Martin, Daniel ; Marx, Philip ; Weidmann, Ben ; Ye, Kadachi Jiada
Institutions:	National Bureau of Economic Research (issuing body)
Publisher:	Cambridge, Mass : National Bureau of Economic Research
Subject:	Künstliche Intelligenz \| Artificial intelligence \| Arbeitsproduktivität \| Labour productivity \| Qualifikation \| Occupational qualification