Interpretability is the effort to understand what is going on inside an AI model - which internal components represent which concepts, and how the model arrives at its outputs. Modern neural networks are not hand-written rules; they are vast collections of learned numbers whose inner workings are opaque even to the people who build them. Interpretability research tries to open that black box. The strand known as mechanistic interpretability goes furthest, attempting to reverse-engineer the specific internal mechanisms a model uses, much as one might trace the circuits of a chip.
A landmark primary is Anthropic’s 2023 publication “Towards Monosemanticity: Decomposing Language Models With Dictionary Learning.” A core difficulty is that individual artificial neurons are typically “polysemantic” - a single neuron lights up for many unrelated concepts at once, which makes it useless as a unit of explanation. The Anthropic team used a technique called a sparse autoencoder to decompose the model’s activity into a larger set of cleaner “features,” each of which corresponds much more closely to a single human-interpretable concept. They presented several lines of evidence that these extracted features are far more monosemantic than the raw neurons, making them a tractable starting point for analyzing how the model works.
This matters because understanding precedes control. If we can identify the internal feature that, say, makes a model deceptive or unsafe, we gain a path to detecting and steering that behavior rather than only observing it after the fact. Interpretability is therefore closely tied to AI safety and alignment.
Why business readers should care: interpretability is what stands between “the model did something and we have no idea why” and “we can audit and explain the model’s behavior.” For regulated industries and high-stakes decisions, the ability to explain and verify a model’s reasoning is increasingly a requirement, not a nicety - and it is an active research frontier rather than a solved problem.