What is interpretability?
The ability to understand and explain the decisions made by an artificial intelligence system
interpretability explained in plain English
Interpretability is about making AI systems transparent, so we can see why they made a particular decision or prediction. It's like being able to look under the hood of a car to see how the engine works.
Analogy
Imagine you asked a friend to recommend a movie, and they said 'I think you'll like this one'. If they can explain why they chose that movie, like 'because it's a romantic comedy and you love those', that's like interpretability. You can understand their reasoning and trust their recommendation more.
Example
For example, an AI system that predicts patient outcomes in a hospital can be more trustworthy if it can explain why it made a particular prediction, such as 'because the patient has a history of heart disease and is showing certain symptoms'.
How is interpretability used?
Common misconceptions about interpretability
Some people think interpretability means the AI system has to be simple or transparent in its inner workings, but that's not necessarily true. Interpretability is about being able to understand the decisions, not the entire system.
History
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