AI Definitions: Explainability

Explainability (or explainable AI; it is similar to but not the same as interpretability or interpretable AI) - While interpretability relates to understanding an AI’s inner workings, explainable AI focus on observed patterns in what the AI does to draw conclusions. Applied after a model has already made its decision or prediction, explainability offers insight into which features or variables played into the outcome in an effort to ensure accuracy, fairness and user trust. Explainability focuses on individual decisions, rather than the model as a whole. Because explainability techniques are applied after the fact, they can be used with any model. On the downside, it can oversimplify a model's decision-making process and make is often difficult for non-experts to understand. Some governments are requiring that AI systems include explainability.

More AI definitions here