Abstract: Machine learning and data analysis have enjoyed tremendous success in a broad range of domains. These advances hold the promise of great benefits to individuals, organizations, and society as a whole. Undeniably, algorithms are informing decisions that reach ever more deeply into our lives, from news article recommendations to criminal sentencing decisions to healthcare diagnostics. This progress, however, raises (and is impeded by) a host of concerns regarding the societal impact of computation. A prominent concern is that left to their own devices, algorithms will propagate - even amplify - existing biases.
Often, definitions of fairness are group-based, typically requiring that a given statistic be equal across a few demographic groups, socially identified as deserving protection. Other definitions aim at individual-level protections. Broad-stroke statistical guarantees tend to be much easier to satisfy (information and complexity theoretically) but tend to be much weaker in the protections they provide.
We will discuss recent research towards bridging the gap between statistical and individual protections. These works provide efficient learning algorithms that ensure protection (according to some fairness notion) to every sub-population within some rich class of sets. Our approach to defining the class of sets to protect is complexity-theoretic. We aim at obtaining the strongest fairness guarantees that can be obtained with the available computational resources.
Based on joint works with Úrsula Hébert-Johnson, Michael P. Kim and Guy Rothblum.
Bio: Omer Reingold is a Professor of Computer Science at Stanford University. Past positions include Samsung Research America, the Weizmann Institute of Science, Microsoft Research, the Institute for Advanced Study in Princeton, NJ, and AT&T Labs. His research is in the Foundations of Computer Science and most notably in Computational Complexity and the Foundations of Cryptography with emphasis on randomness, derandomization and explicit combinatorial constructions. He is an ACM Fellow and among his distinctions are the 2005 Grace Murray Hopper Award and the 2009 Gödel Prize.