3.1 Importance of Interpretability | Interpretable Machine Learning

Machine learning algorithms usually operate as black boxes and it is unclear how they derived a certain decision. This book is a guide for practitioners to make machine learning decisions interpretable.

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related reading

• Dario Amodei — The Urgency of Interpretabilitydarioamodei.com
• 6 – Interpretability – Machine Learning Blog | ML@CMU | Carnegie Mellon Universityblog.ml.cmu.edu
• [1811.10154] Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Insteadarxiv.org
• The Hitchhiker's Guide to Actionable Interpretabilityactionable-interpretability-guide.github.io
• The Building Blocks of Interpretabilitydistill.pub
• How To Become A Mechanistic Interpretability Researcher — AI Alignment Forumalignmentforum.org
• An Ambitious Vision for Interpretability — AI Alignment Forumalignmentforum.org
• Neel Nanda on Mechanistic Interpretability: Progress, Limits, and Paths to Safer AI — EA Forumforum.effectivealtruism.org
• An Extremely Opinionated Annotated List of My Favourite Mechanistic Interpretability Papers v2 — AI Alignment Forumalignmentforum.org
• On Optimism for Interpretabilitygoodfire.ai
• A Pragmatic Vision for Interpretability — AI Alignment Forumalignmentforum.org
• In (highly contingent!) defense of interpretability-in-the-loop ML training — AI Alignment Forumalignmentforum.org