Martin Hirzel
Martin Hirzel is a researcher and the manager of the AI Programming Models team at IBM Research AI. Martin received his PhD from the University of Colorado at Boulder in 2004; his thesis adviser was Amer Diwan. At IBM, Martin works on tools and languages for artificial intelligence and streaming systems. Martin's papers won awards at several conferences and he is an ACM Distinguished Scientist.
Sessions
We would like machine-learning pipelines to be fair, i.e., to avoid bias based on race, gender, age, or other attributes. This talk gives an introduction to algorithmic fairness concepts and shows how to put them into practice with scikit-learn pipelines. You will learn about metrics to measure fairness and about mitigators to reduce bias. Furthermore, you will learn about fairness in the presence of data preprocessing, ensemble learning, hyperparameter tuning, etc. This talk includes code examples based on the Lale open-source library, which provides scikit-learn compatibility for fairness algorithms.