Interpretability Can Be Actionable
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 resea
Record details
Published: 11 May 2026
Source: arXiv
Category: Research
Topics: Safety & alignment
Retrieved: 14 July 2026
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ethics.ai (11 May 2026), “Interpretability Can Be Actionable,” evidence record 4536, https://ethics.ai/record/4536 (originally published by arXiv).
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