The Science of the Predicted Human talk series: Johan Ugander
We are delighted to host Professor Johan Ugander (Stanford University) for a talk in our series on The Science of the Predicted Human. Professor Ugander's empirical and methodological work leverages the unique measurement opportunities created by the internet and digitization to study social networks and human behavior in previously unprecedented ways.
Please join us for this event on May 4, 10am in Goth. Aud. 1, Gothersgade 140.
Title
Harvesting randomness to understand computational social systems
Abstract
Modern social systems are increasingly infused with algorithmic components, designed to optimize various objectives under diverse constraints. Examples include school choice mechanisms to assign students to schools, peer review matching systems to assign papers to reviewers, or targeting strategies in social networks to seed product adoptions. 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 describe general principles 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. By applying these methods to computational social systems, we can gain a deeper understanding of the ways in which these systems operate and their impact on individuals and society as a whole. The talk will incorporate joint work over several years with Alex Chin, Dean Eckles, Yuchen Hu, Steven Jecmen, Samir Khan, Martin Saveski, and Nihar Shah.
About Johan Ugander
Johan Ugander is an Associate Professor of Management Science & Engineering at Stanford University and a member of Stanford's Institute for Computational and Mathematical Engineering. His research primarily focuses on statistical and computational methods for studying social networks, human behavior, and their interplay. Prior to joining the Stanford faculty he was a postdoctoral researcher at Microsoft Research 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, with prior degrees from the University of Cambridge and Lund University. His awards include a 2022 NSF CAREER Award, a 2018 Young Investigator Award from the Army Research Office (ARO), a 2016 Facebook Faculty Award, the 2016 Eugene L. Grant Undergraduate Teaching Award from the Department of Management Science & Engineering, and multiple best paper awards.
The Predicted Human
Being human in 2023 implies being the target of a vast number of predictive infrastructures. In healthcare, algorithms predict not only potential pharmacological cures to disease but also their possible future incidence of those diseases. In governance, citizens are exposed to algorithms that predict - not only their day-to-day behaviors to craft better policy - but also to algorithms that attempt to predict, shape and manipulate their political attitudes and behaviors. In education, children’s emotional and intellectual development is increasingly the product of at-home and at-school interventions shaped around personalized algorithms. And humans worldwide are increasingly subject to advertising and marketing algorithms whose goal is to target them with specific products and ideas they will find palatable. Algorithms are everywhere – as are their intended as well as unintended consequences. The series is arranged with generous support by the Villum Foundation and the Pioneer Center for Artificial Intelligence.