Interpretability Can Be Actionable

2026-05-11

AI Summary

This study addresses the limited real-world impact of current explainability research, which often fails to support actionable decision-making and interventions in practical settings. To bridge this gap, the work proposes a novel, systematic redefinition of explainability centered on "actionability," articulated through two key dimensions: concreteness and verifiability. Building upon this reconceptualization, the authors develop an application-oriented evaluation framework grounded in both conceptual analysis and cross-domain use cases. Through this integrative approach, they identify five distinct domains with high potential for impactful deployment. By prioritizing tangible outcomes over abstract interpretability, the proposed framework offers both theoretical grounding and practical pathways to enhance the real-world relevance and effectiveness of explainable AI research.

Abstract

Interpretability aims to explain the behavior of deep neural networks. Despite rapid growth, there is mounting concern that much of this work has not translated into practical impact, raising questions about its relevance and utility. This position paper argues that the central missing ingredient is not new methods, but evaluation criteria: interpretability should be evaluated by actionability--the extent to which insights enable concrete decisions and interventions beyond interpretability research itself. We define actionable interpretability along two dimensions--concreteness and validation--and analyze the barriers currently preventing real-world impact. To address these barriers, we identify five domains where interpretability offers unique leverage and present a framework for actionable interpretability with evaluation criteria aligned with practical outcomes. Our goal is not to downplay exploratory research, but to establish actionability as a core objective of interpretability research.

Problem

• interpretability
• actionability
• evaluation criteria
• practical impact
• deep neural networks

Innovation

• actionable interpretability
• evaluation criteria
• concreteness
• validation
• practical impact