Intelligible and Explainable Machine Learning: Best Practices and Practical Challenges

Learning methods such as boosting and deep learning have made ML models harder to understand and interpret. This puts data scientists and ML developers in the position of often having to make a tradeoff between accuracy and intelligibility. Research in IML (Interpretable Machine Learning) and XAI (Explainable AI) focus on minimizing this trade-off by developing more accurate interpretable models and by developing new techniques to explain black-box models. Such models and techniques make it easier for data scientists, engineers and model users to debug models and achieve important objectives such as ensuring the fairness of ML decisions and the reliability and safety of AI systems. In this tutorial, we present an overview of various interpretability methods and provide a framework for thinking about how to choose the right explanation method for different real-world scenarios. We will focus on the application of XAI in practice through a variety of case studies from domains such as healthcare, finance, and bias and fairness. Finally, we will present open problems and research directions for the data mining and machine learning community. What audience will learn: When and how to use a variety of machine learning interpretability methods through case studies of real-world situations. The difference between glass-box and black-box explanation methods and when to use them. How to use open source interpretability toolkits that are now available