In data mining we often have to learn from biased data, because, for instance, data comes from different batches or there was a gender or racial bias in the collection of social data. In some applications it may be necessary to explicitly control this bias in the models we learn from the data. Recently this topic received considerable interest both in the research community as well as more general, as witnessed by several recent articles in popular news media such as the New York Times. In this talk I will introduce and motivate research in fairness-aware data mining. Different techniques in unsupervised and supervised data mining will be discussed, dividing these techniques into three categories: algorithms of the first category adapt the input data in such a way to remove harmful biases while the second adapts the learning algorithms and the third category modifies the output models in such a way that its predictions become unbiased. Furthermore different ways to quantify unfairness, and indirect and conditional discrimination will be discussed, each with their own pros and cons. With this talk I hope to convincingly argument the validity and necessity of this often contested research area.