Inherent Trade-Offs in Algorithmic Fairness

Recent discussion in the public sphere about algorithmic classification has involved tension between competing notions of what it means for such a classification to be fair to different groups. We consider several of the key fairness conditions that lie at the heart of these debates, and discuss recent research establishing that except in highly constrained special cases, there is no method that can satisfy all of these conditions simultaneously. These results suggest some of the ways in which key notions of fairness are incompatible with each other, and hence provide a framework for thinking about the trade-offs between them. 

Bio:
His research focuses on issues at the interface of networks and information, with an emphasis on the social and information networks that underpin the Web and other on-line media. My work has been supported by an NSF Career Award, an ONR Young Investigator Award, a MacArthur Foundation Fellowship, a Packard Foundation Fellowship, a Simons Investigator Award, a Sloan Foundation Fellowship, and grants from Facebook, Google, Yahoo!, the ARO, and the NSF. I am a member of the National Academy of Sciences, the National Academy of Engineering, and the American Academy of Arts and Sciences.