Preference for Explainable AI

Alex Chan

No 35240, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: Participants acted as loan officers deciding whether to approve real $10,000-loans issued by a private U.S. lender using an AI’s default-risk predictions. When explanations revealed that the AI penalized non-White or female borrowers, participants were more likely to override the AI’s profit-maximizing recommendation. When their bonuses depended on repayment, however, they sought predictions but avoided explanations, consistent with willful ignorance; this effect faded when explanations were framed as purely financial or demographics were hidden. A secondary experiment reveals a novel bias: participants failed to reason contingently and undervalued explanations even when these complemented private information and improved decision accuracy.

JEL-codes: B4 C1 C91 C92 D12 D14 D81 G21 G41 (search for similar items in EconPapers)
Date: 2026-05
Note: LE PE POL
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