Invited Talk

Krishna Gummadi

Head, Networked Systems Research Group, Max Planck Institute

Abstract: Discrimination in Human vs. Algorithmic Decision Making

Algorithmic (data-driven) decision making is increasingly being used to assist or replace human decision making in a variety of domains ranging from banking (rating user credit) and recruiting (ranking applicants) to judiciary (profiling criminals) and journalism (recommending news-stories). Against this background, in this talk, I will pose and attempt to answer the following high-level questions:

(a) Can algorithmic decision making be discriminatory?

(b) Can we detect discrimination in decision making?

(c) Can we control algorithmic discrimination? i.e., can we make algorithmic decision more fair?


Krishna Gummadi is a tenured faculty member and head of the Networked Systems research group at the Max Planck Institute for Software Systems (MPI-SWS) in Germany. He received his Ph.D. degree in Computer Science and Engineering from the University of Washington, Seattle.  His current projects focus on understanding and building social Web systems. Specifically, they tackle the challenges associated with protecting the privacy of users sharing personal data, understanding and leveraging word-of-mouth exchanges to spread information virally, and finding relevant and trustworthy sources of information in crowds. His recent works attempt to understand and enhance fairness and transparency of data-driven (algorithmic) decision making increasingly being used to model and replace human decision making in social computing systems.

Krishna's work on social computing systems, Internet access networks, and peer-to-peer systems has led to a number of widely cited papers, including award (best) papers at ACM COSN, ACM/Usenix's SOUPS, AAAI's ICWSM, Usenix's OSDI, ACM's SIGCOMM IMC, and SPIE's MMCN conferences. He has also co-chaired AAAI's ICWSM 2016, IW3C2 WWW 2015, ACM COSN 2014, and ACM IMC 2013 conferences.