The course is focused on studying the problems of fairness, accountability, confidentiality, and transparency (FACT) in data science, and data mining and machine learning in particular. One important challenge to face is that machine learnt models typically are not 100% accurate, i.e. in some ways these models are wrong. Thus it is important to study how we can make a good use of models that are not perfect, how we can understand the strengths and weaknesses of these models, how we can help a decision maker to trust (or not trust) the model or its particular prediction, and how we can get insights into impact of input features and some inner logic of a predictive model. We need techniques not just to explain the decision of a model, but also to uncover and characterize undesired or even unlawful biases in its performance. Hence, the other important challenge to study is how to formally define such biases, how to uncover and quantify them and how to design machine learning solutions that would enable the so-called fair algorithmic decision making by design. On the other side of the spectrum, there are challenges of privacy and confidentiality. We will study the main principles and techniques that have been researched and employed in data mining for privacy-preserving and secure computation to induce models from data and to apply them in real-life scenarios.