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

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

No 33021, NBER Working Papers from National Bureau of Economic Research, Inc

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.

JEL-codes: D81 J24 (search for similar items in EconPapers)
Date: 2024-10
New Economics Papers: this item is included in nep-ain, nep-exp, nep-hrm and nep-lma
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