Machine learning has taken over our world, in more ways than we realize. You might get book recommendations, or an efficient route to your destination, or even a winning strategy for a game of Go. But you might also be admitted to college, granted a loan, or hired for a job based on algorithmically enhanced decision-making. We believe machines are neutral arbiters: cold, calculating entities that always make the right decision, that can see patterns that our human minds can’t or won’t. But are they? Or is decision-making-by-algorithm a way to amplify, extend and make inscrutable the biases and discrimination that is prevalent in society?

To answer these questions, we need to go back — all the way to the original ideas of justice and fairness in society. We also need to go forward — towards a mathematical framework for talking about justice and fairness in machine learning. I will talk about the growing landscape of research in algorithmic fairness: how we can reason systematically about biases in algorithms, and how we can make our algorithms fair(er).

Course Mechanics

This is a short (and intense) course. We’ll cover material in two lecture chunks each day. But this is also a discussion, on a topic that’s still very new and that has fluid boundaries and evolving formalisms. I’ve provided readings that are technical and non-technical in nature, and I expect (and hope!) that the presentations will provoke discussion, arguments, and new ideas.

So please read the provided materials ahead of the lecture and come prepared with your questions, comments and critiques. You’ll benefit the most from the material if you have time to engage with it.


Dec 11: Preliminaries

Basics of machine learning – supervised and unsupervised learning, empirical risk minimization, classifiers, regression, training and generalization.

Dec 12: Automated Decision Making: Case studies of the use of machine learning in applications. An introduction to different formal notions of fairness.

Readings (notions of fairness):

Dec 13: Fairness Mechanisms: Understanding the different techniques for ensuring fairness in classification.

Dec 14: Fairness Mechanisms (continued)

Dec 15: Accountability via Influence Estimation: Probing black-box decision-makers: estimating influence of features.

Dec 17: Interpretability: Building interpretable models.

Dec 18: Fairness, Accountability and Transparency in other areas of computer science: Beyond classification: unsupervised learning, representations, rankings and verification.

Dec 19: Belief Systems: Axiomatic approaches to thinking about fairness.


Email me at suresh@cs.utah.edu

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