Interpretability and Explainability in Machine Learning

As machine learning models are increasingly being employed to aid decision makers in high-stakes settings such as healthcare and criminal justice, it is important to ensure that the decision makers correctly understand and consequent trust the functionality of these models. This graduate level course aims to familiarize students with the recent advances in the emerging field of explainable ML. In this course, we will review seminal position papers of the field, understand the notion of model interpretability from the perspective of various kinds of end users (e.g., doctors, ML researchers/engineers), discuss in detail different classes of interpretable models and model explanations (e.g., case/prototype based approaches, sparse linear models, rule-based techniques, saliency maps, generalized additive models, and counterfactual explanations), and explore the connections between model interpretability and fairness, robustness, causality and debugging. The course will also emphasize on various applications which can immensely benefit from model interpretability including medical and judicial decision making.

The course will comprise of a mix of lectures by instructor, paper presentations by students, and guest lectures by researchers who have authored seminal papers on this topic. This course has a significant research component in the form of a course project that students will be expected to work on through out the semester. All in all, this course is geared towards those students who are interested in diving deep and conducting research in the field of interpretable and explainable ML.