Krishna Gummadi - Discrimination in Human vs. Algorithmic Decision Making

Organisé par : 
L’équipe "Keynotes" du LIG
Intervenant : 
Krishna Gummadi, MPI-SWS


Krishna Gummadi is a tenured faculy 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.

Krishna's research interests are in the measurement, analysis, design, and evaluation of complex Internet-scale systems. His current projects focus on understanding and building social computing systems. Specifically, they tackle the challenges associated with  protecting the privacy of users sharing data online, understanding and leveraging word-of-mouth exchanges to spread information virally, and finding relevant and trustworthy sources of information in online crowds.
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. Krishna has also co-chaired AAAI's ICWSM 2016, IW3C2 WWW 2015, ACM COSN 2014, and ACM IMC 2013 conferences.
"Réalisation technique : Antoine Orlandi | Tous droits réservés"

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 algorithmic discrimination be controlled? i.e., can algorithmic decision making be made more fair?
(c) Can algorithmic decisions be used to detect and avoid implicit biases in human decisions?