Explainable AI is a recent and growing subfield of machine learning (ML) that aims to bring transparency into ML models without sacrificing their predictive accuracy. This seminar will explore current research on the use of Explainable AI for building models whose decisions are more trustworthy. Techniques to verify existing models and to correct flaws identified by the user from explanations will be covered. Students will select a few papers from a pool of thematically relevant research papers, which they will read and present over the course of the semester.