Interpretability methods examine features, internal activations, relationships or decision processes to make aspects of a model understandable. Some approaches describe observed patterns, while others test whether changing a component changes the output.
An understandable explanation is not automatically a complete or causal account. Findings may apply only to particular inputs, features or models, so independent checks and stated limits remain important.
ELI5
Interpretability is an attempt to understand what is happening inside a complicated AI instead of judging it only by the final answer. It is similar to examining both a machine's output and the parts that influenced it.
For example, a researcher might test which internal features change when an AI recognizes a risky instruction. That clue can help investigation, but it does not automatically explain every decision.
Does interpretability explain every answer?
No. A method may reveal only a small part of a model's behavior or work only under certain conditions.
Why does it matter for safety?
Understanding internal mechanisms can help investigate failures, but it does not by itself guarantee safe behavior.
