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Public attitudes value interpretability but prioritize accuracy in Artificial Intelligence

ABSTRACT: As Artificial Intelligence (AI) proliferates across important social institutions, many of the most powerful AI systems available are difficult to interpret for end-users and engineers alike. Here, we sought to characterize public attitudes towards AI interpretability. Across seven studies (N = 2475), we demonstrate robust and positive attitudes towards interpretable AI among non-experts that generalize across a variety of real-world applications and follow predictable patterns. Participants value interpretability positively across different levels of AI autonomy and accuracy, and rate interpretability as more important for AI decisions involving high stakes and scarce resources. Crucially, when AI interpretability trades off against AI accuracy, participants prioritize accuracy over interpretability under the same conditions driving positive attitudes towards interpretability in the first place: amidst high stakes and scarce resources. These attitudes could drive a proliferation of AI systems making high-impact ethical decisions that are difficult to explain and understand. For many AI systems, it is hard to interpret how they make decisions. Here, the authors show that non-experts value interpretability in AI, especially for decisions involving high stakes and scarce resources, but they sacrifice AI interpretability when it trades off against AI accuracy.

SUBMITTER: Nussberger A

PROVIDER: S-EPMC9528860 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature

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Public attitudes value interpretability but prioritize accuracy in Artificial Intelligence

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