Explainability:

Explainability provides us with algorithms that give insights into trained model predictions. It allows us to answer questions such as:

• How does a prediction change dependent on feature inputs?
• What features are or are not important for a given prediction to hold?
• What set of features would you have to minimally change to obtain a new prediction of your choosing?
• How does each feature contribute to a model’s prediction?