The explainability of an algorithm has special importance for machine learning algorithms. In many applications of machine learning, users are asked to trust a model to help them to make decisions. Explainability provides transparency when needed in such use cases. 

Let's look deeper at a specific example. Let's assume that we want to use machine learning to predict the prices of houses in the Boston area based on their characteristics. Let's also assume that local city regulations will allow us to use machine learning algorithms only if we can provide detailed information for the justification of any predictions whenever needed. This information is needed for audit purposes to make sure that certain segments of the housing market are not artificially manipulated. Making our trained model explainable will provide this additional information.

Let's look into different options that are available for implementing the explainability of our trained model.