As machine learning is increasingly being deployed in real world applications, it has become critical to ensure that stakeholders understand and trust these models. End users must have a clear understanding of the model behavior so they can diagnose errors and potential biases in these models, and decide when and how to employ them. However, most accurate models that are deployed in practice are not interpretable, making it difficult for users to understand where the predictions are coming from, and thus, difficult to trust. Recent work on explanation techniques in machine learning offers an attractive solution: they provide intuitive explanations for “any” machine learning model by approximating complex machine learning models with simpler ones. In this talk, I will discuss several popular post hoc explanation methods, and shed light on their advantages and shortcomings. I will conclude the tutorial by discussing implications for recommender systems and highlighting open research problems in the field