Abstract: In this talk I will give an overview of the role of causality in ethical machine learning, and in particular, in fair and explainable ML. In particular, I will first detail how to use causal reasoning to study fairness and interpretability problems in algorithmic decision making, stressing the main limitations that we encounter when aiming to address these problems in practice. Then, I will provide some hints about how to solve some of these practical limitations by using causal generative models. A novel class of deep generative models that do not only accurately fit observational data but can also provide accurate estimates to interventional and counterfactual queries. I will finally discuss the open challenges of designing such causal generative models.
Speaker: Isabel Valera is Full Professor of Machine Learning at the Department of Computer Science at Saarland University (Saarbrücken, Germany), and Adjunct Faculty at the MPI for Software Systems in Saarbrücken (Saarbrücken, Germany). She is the recipient of an ERC Starting Grant on “Society-Aware ML”, and a Fellow of the European Laboratory for Learning and Intelligent Systems (ELLIS). Previously, she was an independent group leader at the MPI for Intelligent Systems in Tübingen, Germany. She received her Ph.D. in 2014 and her MSc in 2012 from the University Carlos III in Madrid, Spain, and worked as a postdoctoral researcher at the MPI for Software Systems (Germany) and the University of Cambridge (UK). Her research focuses on the development of trustworthy machine learning methods that can be used in the real world. Her research can be broadly categorized into three main themes: fair, interpretable, and robust machine learning. Her research interests cover a wide range of ML approaches, including deep learning, probabilistic modeling, causal inference, time series analysis, and many more.
Recording: Causethical ML: from theory to practice