Explanations are a means to an end

• URL: http://arxiv.org/abs/2506.22740v1
• Date: Sat, 28 Jun 2025 03:04:21 GMT
• Title: Explanations are a means to an end
• Authors: Jessica Hullman, Ziyang Guo, Berk Ustun,
• Abstract summary: We argue that explanations should be designed and evaluated with a specific end in mind.<n>We show how this functionally-grounded approach can be applied across diverse use cases.
• Score: 19.858633174167334
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Modern methods for explainable machine learning are designed to describe how models map inputs to outputs--without deep consideration of how these explanations will be used in practice. This paper argues that explanations should be designed and evaluated with a specific end in mind. We describe how to formalize this end in a framework based in statistical decision theory. We show how this functionally-grounded approach can be applied across diverse use cases, such as clinical decision support, providing recourse, or debugging. We demonstrate its use to characterize the maximum "boost" in performance on a particular task that an explanation could provide an idealized decision-maker, preventing misuse due to ambiguity by forcing researchers to specify concrete use cases that can be analyzed in light of models of expected explanation use. We argue that evaluation should meld theoretical and empirical perspectives on the value of explanation, and contribute definitions that span these perspectives.

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