Artificial Intelligence is complex. It’s hard to understand how it works and which are internal logic applied by a neural network: that’s why it is typically defined as a black box.

Explainable AI (XAI) refers to methods and techniques in AI application providing results and solutions understandable by humans. Can be defined also as the ability to understand how Artificial Intelligence thinks. It is a new visual and scientific approach to artificial thinking.

It is strictly fundamental to be aware of the internal mechanism of a neural network applied to our business solutions for accounting, responsibility purpose and to prevent possible malfunctions, that can lead also to unmanageable disasters.

Out of the box, our Artificial Intelligence algorithms have the native explainability layer in order to be compliant with the requirement of the European Union. (https://ec.europa.eu/jrc/en/publication/robustness-and-explainability-artificial-intelligence).

AI explainability example
Explainability maps example applied to ECG time series.