AI Definitions: Interpretability

Interpretability (or interpretable AI which is similar but not the same as explainability and explainable AI) – The study of how to understand and explain the decisions made by artificial intelligence (AI) systems in order to audit them for safety and biases. It is a key ingredient of human-centered design because a more transparent model is usually more trustworthy—it's easier (than explainable AI) to verify and evaluate as well as easier and quicker to debug and optimize. However, this transparency through its inner workings can impact performance, especially when dealing with complex models, like neural networks. Interpretability techniques include decision trees, linear regression, scalable Bayesian rule lists, etc.

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