The Hitchhiker's Guide to Actionable Interpretability

Understanding models is important—but understanding alone isn't enough. We make the case that interpretability research should be evaluated by what it enables: concrete decisions, interventions, and improvements beyond the field itself. Hadas Orgad Kempner Institute, Harvard Fazl Barez University of Oxford, Martian Tal Haklay Technion Isabelle Lee USC Marius Mosbach Mila, McGill Anja Reusch Technion Naomi Saphra Kempner Institute, Harvard, Boston University Byron C. Wallace Northeastern University Sarah Wiegreffe University of Maryland Eric Wong University of Pennsylvania Ian Tenney Google DeepMind Mor Geva Tel Aviv University Feb. 17, 2026 No DOI yet. There are hundreds of interpretability papers published every year. Probing, circuits, sparse autoencoders, feature attribution — the field is thriving. This growth is driven by the intuition that if we understand how models work, we should be able to make them safer, more reliable, and better aligned with what we actually want. But here

The Hitchhiker's Guide to Actionable Interpretability The Hitchhiker's Guide to Actionable Interpretability Understanding models is important—but understanding alone isn't enough. We make the case that interpretability research should be evaluated by what it enables: concrete decisions, interventions, and improvements beyond the field itself. 📄 Blog companion to: "Interpretability Can Be Actionable" [PDF] arXiv:2605.11161 📅 February 2026 This post is a companion to our paper. It covers the key ideas; see the full paper for the complete analysis and references. Our framing draws in part on di

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