Interpretability and explainability are closely related

Explanations may be generated before the model is built, also called ex-ante or the model is trained and tested first and then the explanation may be generated, also called post-hoc.

Visual or graphical explanations, tabular data-driven explanation, natural language descriptions or voice explanations are some of the existing modes of explanation.

A salesperson might be comfortable with an explanation that shows a graph of increasing sales and how the increase in sales i...

End users require an explanation of the decision or action recommended made by the AI system in order to carry out the recommendation.

Business users require an explanation to ensure corporate governance and manage reputational risk to their group or company.

Data scientists require...

The audience for the explanation or whom to explain should be the first question to answer. Understanding the motivation of the audience, what action or decision the audience is planning to make, their mathematical or technical knowledge and expertise are all important aspects that should be cons...

There are six broad approaches as it relates to post-hoc explainability.

• Feature relevance: These approaches to explainability focus on the inner functioning of the model and highlight the features that best explain the outcome of the model.
• Model simplification: These approach...