A growing collection of resources about Machine Learning eXplainability. Our objective is to cover both theory and practice, including:

• high-level overviews & introductory examples;
• mathematical foundations;
• algorithmic implementations;
• practical advice & real-life caveats; and
• success & failure case studies.

We will be adding more eXplainable Machine Learning techniques to our compendium over time, so stay tuned. Cannot wait? Contribute a technique description yourself.