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Poster
Average Individual Fairness: Algorithms, Generalization and Experiments
Saeed Sharifi-Malvajerdi · Michael Kearns · Aaron Roth

Thu Dec 12 05:00 PM -- 07:00 PM (PST) @ East Exhibition Hall B + C #106
in Applications -- Fairness, Accountability, and Transparency »

We propose a new family of fairness definitions for classification problems that combine some of the best properties of both statistical and individual notions of fairness. We posit not only a distribution over individuals, but also a distribution over (or collection of) classification tasks. We then ask that standard statistics (such as error or false positive/negative rates) be (approximately) equalized across individuals, where the rate is defined as an expectation over the classification tasks. Because we are no longer averaging over coarse groups (such as race or gender), this is a semantically meaningful individual-level constraint. Given a sample of individuals and problems, we design an oracle-efficient algorithm (i.e. one that is given access to any standard, fairness-free learning heuristic) for the fair empirical risk minimization task. We also show that given sufficiently many samples, the ERM solution generalizes in two directions: both to new individuals, and to new classification tasks, drawn from their corresponding distributions. Finally we implement our algorithm and empirically verify its effectiveness.

Keywords: Learning Theory · Fairness, Accountability, and Transparency · Applications · Theory
[ Paper]  [ Slides] 

Author Information

Saeed Sharifi-Malvajerdi (University of Pennsylvania)
Michael Kearns (University of Pennsylvania)

Michael Kearns is Professor and National Center Chair in the Computer and Information Science department at the University of Pennsylvania. His research interests include topics in machine learning, algorithmic game theory, social networks, and computational finance. Prior to joining the Penn faculty, he spent a decade at AT&T/Bell Labs, where he was head of AI Research. He is co-director of Penn’s Warren Center for Network and Data Sciences (warrencenter.upenn.edu), and founder of Penn’s Networked and Social Systems Engineering (NETS) undergraduate program (www.nets.upenn.edu). Kearns consults extensively in technology and finance, and is a Fellow of the Association for the Advancement of Artificial Intelligence and the American Academy of Arts and Sciences.

Aaron Roth (University of Pennsylvania)

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