Interpretability

What is Interpretability?

Interpretability — specifically mechanistic interpretability — is the science of understanding what happens inside neural networks. Rather than treating AI models as opaque black boxes that take inputs and produce outputs, interpretability researchers reverse-engineer the internal computations to understand how and why models behave the way they do.

Think of it like the difference between knowing a car goes when you press the gas pedal versus understanding the internal combustion engine. Interpretability aims to open the hood of AI systems and map out their internal mechanisms — the features they detect, the circuits they form, and the representations they build.

Key Characteristics

• Mechanistic: Focuses on understanding the actual computational mechanisms, not just statistical correlations
• Feature-level: Identifies what individual neurons and groups of neurons represent (e.g., "this neuron activates for sarcasm")
• Circuit-level: Maps how information flows through the network to produce specific behaviors
• Safety-critical: Enables verification of safety properties by understanding what the model is actually doing internally
• Scalability challenge: Current techniques work well on smaller models but are being scaled to frontier models

Why Interpretability Matters

For organizations deploying AI, interpretability offers something no other safety technique provides: understanding. Evaluations can tell you what a model does in tested scenarios, but interpretability can tell you why — and potentially predict behavior in untested scenarios.

This has direct practical applications. If you can identify the internal circuits responsible for harmful behavior, you can intervene directly rather than playing whack-a-mole with outputs. It also builds trust: customers and regulators are more likely to trust AI systems whose behavior can be explained mechanistically rather than just statistically.

Anthropic co-founder Dario Amodei has suggested the work could have implications far beyond AI safety — if the techniques for understanding artificial neural networks can be applied to biological neural networks, it could accelerate neuroscience and medical research.

Historical Context

The field was pioneered largely by Chris Olah, who began publishing influential work on neural network visualization during his time at Google Brain (2015-2021). His blog posts and the research journal distill.pub set new standards for making ML research visually intuitive and accessible. When Olah co-founded Anthropic in 2021, interpretability became one of the company's core research pillars alongside scaling and alignment.

Key milestones include the discovery of "features" (meaningful internal representations), "circuits" (computational pathways), and more recently, "sparse autoencoders" that can decompose model activations into interpretable components at scale.

Related Reading

• Chris Olah - Pioneer of mechanistic interpretability
• Scaling Laws - Related Anthropic research on model behavior
• Responsible Scaling Policy - Framework that interpretability supports
• Deep Learning - The field interpretability aims to explain