{
  "id": 4536,
  "url": "https://arxiv.org/abs/2605.11161v1",
  "title": "Interpretability Can Be Actionable",
  "summary": "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",
  "authors": "Hadas Orgad, Fazl Barez, Tal Haklay, Isabelle Lee, Marius Mosbach, Anja Reusch et al.",
  "category": "research",
  "topics": "safety-alignment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-05-11T19:08:21.000Z",
  "fetched_at": "2026-07-14T16:31:03.580Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/4536",
  "original_url": "https://arxiv.org/abs/2605.11161v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}