Explainability

Explainability provides businesses the opportunity to communicate more clearly with their customers too – a win-win in all scenarios.

For example, in a use case like credit determination, explainability can inform future credit risk models.

While the desire for AI explainability is understandable, its importance is often overstated.

Key to achieving this is ‘explainability’ (why and how the expected outcome is going to happen) and ‘interpretability’ (how effectively you can predict the outcome).

One way to gain explainability in AI systems is to use machine learning algorithms that are inherently explainable.