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Algorithmic Fairness: Measures, Methods and Representations

What happens when we replace - or augment - human decision-making with algorithms? This is a simple question, but the answers now define a new field of study - a field that I call algorithmic fairness, and that spans issues of fairness, discrimination, accountability, transparency, interpretability and responsibility, and so much more. While some of the early work in the area came out of data mining and machine learning, the field is now truly transdisciplinary, with contributions from all across computer science, as well as from all disciplines that touch on aspects of society - whether it be economics, philosophy, sociology, political science, or communication. In this tutorial, I'll try to do three things: I'll survey the main questions and some of the key insights we've developed over the years. I'll explain the web of connections between the technical and the social disciplines that make up this area, and I'll point to exciting directions that remain to be explored in both technical and social dimensions. Along the way I hope to illustrate what I think are some interesting "collisions" between computer science and the social sciences, and call for a reimagining of core ideas in our field, including the very idea of how we think about data representation.