Explanations are a means to an end

2025-06-27

AI Summary

Existing interpretability research in machine learning predominantly focuses on input-output mapping mechanisms, neglecting the functional role of explanations within concrete application contexts—such as clinical decision support, model debugging, or remedial intervention—leading to potential misuse. Method: This paper proposes a “use-case-driven” paradigm grounded in statistical decision theory, establishing a quantifiable analytical framework for explanation utility: it formally defines the maximum performance gain an explanation can yield in a given task and characterizes its theoretical utility upper bound, while unifying evaluation criteria across diverse application scenarios. Contribution/Results: The framework shifts interpretability research from qualitative description toward a rigorous, analyzable, verifiable, and reproducible scientific paradigm. It significantly enhances the reliability and practical utility of explanation methods in real-world decision-making environments.

Technology Category

• Machine Learning: Transparent, Interpretable, Explainable ML
• Computer Vision: Interpretability, Explainability, and Transparency
• Humans and AI: Explainable AI (XAI) for Human Understanding

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

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.

Problem

• Design explanations for specific practical uses
• Formalize explanation goals using decision theory
• Evaluate explanations via theoretical and empirical methods

Innovation

• Framework based in statistical decision theory
• Functionally-grounded approach for diverse use cases
• Evaluation melds theoretical and empirical perspectives