Explainability in Machine Learning: A Pedagogical Perspective

Andreas Bueff¹Ioannis Papantonis^2*Auste Simkute³Vaishak Belle²

• ¹Linköping University, Linköping, Östergötland, Sweden
• ²School of Informatics, University of Edinburgh, Edinburgh, United Kingdom
• ³University of Edinburgh, Edinburgh, Scotland, United Kingdom

Machine learning courses usually focus on getting students prepared to apply various models in real-world settings, but much less attention is given to teaching students the various techniques one could employ to explain a model's decision-making process. In an attempt to fill this gap, we provide a pedagogical perspective on how to structure a course to better impart knowledge to students and researchers in machine learning, about when and how to implement various explainability techniques as well as how to interpret the results. We discuss a system of teaching explainability in machine learning, by exploring the advantages and disadvantages of various opaque and transparent machine learning models, as well as when to utilise specific explainability techniques and the various frameworks used to structure the tools for explainability. We also discuss ways to structure assignments to best help students learn to use explainability as a tool alongside any given machine learning application.