The Sequence Knowledge #701: Not All Types of AI Interpretability are Created Equal
Understanding the different types of AI interpretability.
Today we will Discuss:
We explore the different types of AI interpretability.
We review Activation Atlases, one of the most famous papers ever written about AI interpretability.
💡 AI Concept of the Day: Different Types of AI Interpretability
Interpretability in modern AI spans a spectrum of approaches, each aiming to illuminate different facets of how complex models arrive at their outputs. Broadly, we can categorize these methods into three families: post-hoc explainability, intrinsic interpretability, and mechanistic interpretability. Though they share the common goal of demystifying “black-box” neural networks, they differ fundamentally in when and how they extract insights: after training, during design, or by dissecting learned structures. Understanding these distinctions is crucial for selecting the right toolset when debugging, auditing, or aligning high-capacity frontier models.