Platform and Model Design for Responsible AI

9

Model Explainability

“If you can’t explain it simply, you don’t understand it well enough.”

– Albert Einstein

Model explainability is an important topic in the fields of Machine Learning (ML) and Artificial Intelligence (AI). It refers to the ability to understand and explain how a model makes predictions and decisions. Explainability is important because it allows us to identify potential biases or errors in a model, and it can improve the performance and trustworthiness of AI models.

In this chapter, we will explore different methods and techniques for explaining and interpreting ML models. We will also examine the challenges and limitations of model explainability and will consider potential solutions to improve the interpretability of ...