AI Safety & Responsible AI
Interpretability
How understandable a model's internal reasoning or decision process is to humans.
Example
Researchers examine internal model behavior to understand why it produced a result.
Why it matters
It helps people inspect how predictions depend on the system's design or learned patterns.
You'll hear it
“Can we follow how these clues lead to this decision?”
Can you control it?
Not directly
No, not directly. Interpretability describes a learned property, observed result, or broader concept rather than a normal user setting. You may influence it indirectly by changing the tool, data, prompt, or workflow.
Common questions
- Is an interpretable system always accurate?
- No. Understandable logic can still be wrong.
- Can a simpler system be easier to interpret?
- Often, yes. But simplicity alone does not guarantee that every result is easy to explain.
- Is this the same as seeing the source code?
- No. Access to code does not necessarily make learned behavior understandable.