Interpretability

Interpretability is the property of an AI model that allows humans to understand how it makes decisions. While related to explainability, interpretability typically refers to deeper, mechanistic understanding of the model itself — how internal computations work — rather than post-hoc explanations of individual predictions. Inherently interpretable models include linear regression, decision trees of modest size, and rule-based systems where each step is human-readable. Black-box models like deep neural networks require post-hoc interpretability methods. Mechanistic interpretability research at Anthropic, OpenAI, DeepMind, and academic labs aims to reverse-engineer neural networks at the neuron and circuit level, with notable progress on understanding what specific transformer features detect. Real-world examples include circuit analyses of vision models (the Distill.pub series) and the SAE-based feature analyses of large language models. AI governance and AI compliance programs increasingly require interpretability evidence for high-risk responsible AI deployments, supporting AI risk management and AI accountability in regulated industries.

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