Designing Human-AI Collaboration: A Sufficient-Statistic Approach

Nikhil Agarwal, Alex Moehring and Alexander Wolitzky

No 33949, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: We propose a sufficient statistic for designing AI information-disclosure and selective automation policies. The approach allows for endogenous and biased beliefs, and effort crowd-out, without using a structural model of human decision-making. We deploy and validate our approach in a fact-checking experiment. Humans under-respond to AI predictions and reduce effort when presented with confident AI predictions. Overconfidence in own-signal rather than under-confidence in AI drives AI under-response. The optimal policy automates decisions where the AI is confident and delegates the other decisions while fully disclosing the AI prediction. Although automation is valuable, the benefit of assisting humans with AI is negligible.

JEL-codes: C91 D47 D83 D89 (search for similar items in EconPapers)
Date: 2025-06
New Economics Papers: this item is included in nep-ain and nep-exp
Note: IO PR
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Citations: View citations in EconPapers (2)

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