When people rely on A.I. to avoid ethical challenges

 HBS puts the spotlight on a paper by Alex Chan.

When AI Gives Advice, Employees Rarely Ask Why   Featuring Alex Chan. By Ben Rand

"People increasingly trust AI to make decisions—but research by Alex Chan finds they avoid evaluating the algorithm's rationale if it causes moral discomfort. How can organizations encourage employees to think more critically? "

 

Here's the paper:

Preference for Explanations: Case of Explainable AI
By: Alex Chan   Harvard Business School Working Paper, No. 26-028, November 2025.


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 disappeared 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.