Explanations are a means to an end
This work addresses the problem of ambiguous and misused explanations in ML for researchers and practitioners, offering a more targeted evaluation method, though it is incremental in refining existing explanation paradigms.
The paper argues that explainable machine learning methods should be designed and evaluated with specific practical ends in mind, proposing a framework based on statistical decision theory to formalize this approach and demonstrate its application across diverse use cases like clinical decision support.
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.