In this lecture, we will address the challenges of black box models in machine learning in relation to trustworthiness, fairness and utility of AI application. We will examine the mechanics of specific models, showing, what makes some models interpretable, and how they become obscure. The session will also cover common technical solutions to mitigate black box issues and present illustrative use cases for these techniques, along with an overview of the current challenges in the field.