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Tutorial
Representation Learning and Fairness
Moustapha Cisse · Sanmi Koyejo

Mon Dec 09 02:45 PM -- 04:45 PM (PST) @ West Exhibition Hall A

It is increasingly evident that widely-deployed machine learning models can lead to discriminatory outcomes and can exacerbate disparities in the training data. With the accelerating adoption of machine learning for real-world decision-making tasks, issues of bias and fairness in machine learning must be addressed. Our motivating thesis is that among a variety of emerging approaches, representation learning provides a unique toolset for evaluating and potentially mitigating unfairness. This tutorial presents existing research and proposes open problems at the intersection of representation learning and fairness. We will look at the (im)possibility of learning fair task-agnostic representations, connections between fairness and generalization performance, and the opportunity for leveraging tools from representation learning to implement algorithmic individual and group fairness, among others. The tutorial is designed to be accessible to a broad audience of machine learning practitioners, and the necessary background is a working knowledge of predictive machine learning.

Author Information

Moustapha Cisse (Google Brain)
Moustapha Cisse

Moustapha Cisse is a research scientist at Google AI where he works on foundational machine learning and its applications to solving complex societal challenges. Moustapha is also a Professor of Machine Learning at the African Institute of Mathematical Sciences where he is the Founder and Director of the African Masters of Machine Intelligence (AMMI). He holds a PhD in Machine Learning from Pierre et Marie Curie University, France. He was previously a Research Scientist at Facebook AI.

Sanmi Koyejo (UIUC)
Sanmi Koyejo

Sanmi Koyejo is an Assistant Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign and a research scientist at Google AI in Accra. Koyejo's research interests are in developing the principles and practice of adaptive and robust machine learning. Additionally, Koyejo focuses on applications to biomedical imaging and neuroscience. Koyejo co-founded the Black in AI organization and currently serves on its board.

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