Modern social systems are increasingly infused with algorithmic components, designed to optimize various objectives under diverse constraints. Examples include content ranking algorithms in social media, review assignment algorithms in academic peer review, targeting strategies in social networks to seed product adoptions, and school choice mechanisms to assign students to schools. In many such systems (and in all of these examples), such algorithms are commonly randomized, motivated by fairness, strategic, or efficiency considerations. In this talk, I will argue for how such randomness can be harvested to make causal inferences not only about the effects of these systems on various outcomes, but also how the system would behave under alternative algorithmic designs, a version of off-policy evaluation. These evaluations come with unique methodological challenges, notably overlap that is often weak or nonexistent, but by developing novel methods to overcome these challenges we can gain confidence about the ways in which these systems operate and their impact on individuals and society as a whole.

Bio:
Johan Ugander is an Associate Professor at Stanford University in the Department of Management Science & Engineering and the Institute for Computational & Mathematical Engineering, within the School of Engineering. His research develops computational and statistical methods to study social networks, human behavior, and their interplay. Prior to joining the Stanford faculty he was a postdoctoral researcher at Microsoft Research Redmond 2014-2015 and held an affiliation with the Facebook Data Science team 2010-2014. He obtained his Ph.D. in Applied Mathematics from Cornell University in 2014. His awards include a NSF CAREER Award, a Young Investigator Award from the Army Research Office (ARO), three Best Paper Awards (2012 ACM WebSci Best Paper, 2013 ACM WSDM Best Student Paper, 2020 AAAI ICWSM Best Paper), and the 2016 Eugene L. Grant Undergraduate Teaching Award from the Department of Management Science & Engineering.