Anthropic publishes the Interpretability team page with a clear message: to make AI safe, we first need to understand it. Why does that matter? Because if you don't know what's happening inside a large model, it's very hard to predict or mitigate unwanted behaviors — from biases to harmful autonomous actions.
Objective and focus of the team
The team's goal is ambitious but simple: be able to explain in detail how large language models behave, and use that explanation to solve practical safety and alignment problems. Sounds straightforward, but how do you get there?
They take a multidisciplinary approach: researchers trained in mechanistic interpretability, authors of work on scaling laws, and people who came from astronomy, physics, biology, math, and data visualization.
Why does that mix matter to you? Because understanding neural networks isn't just about measuring accuracy: it's about tracing internal circuits, spotting activation patterns, and seeing how those patterns encode behavioral traits. Think of it like diagnosing a car: you don't just read the speedometer, you open the hood.
