Interpretability methods investigate the internal representations, computations, or decision factors associated with an AI system's output. Some methods explain a particular result, while others look for broader mechanisms or recurring patterns inside a model.
Interpretability can help researchers diagnose failures, test safety hypotheses, and form better questions for evaluation. An explanation is still evidence with limits rather than complete access to a model's reasoning, so it should be validated and combined with behavioral testing.
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.
Is interpretability the same as a model explaining itself?
Not necessarily. A generated explanation may be inaccurate, while interpretability research uses methods designed to study behavior or internal mechanisms directly.
Can interpretability guarantee safe behavior?
No. It can provide useful evidence and reveal problems, but it does not replace testing, monitoring, or other safeguards.
