Dates: 18, 20 & 21 February 2019
Venues: London, Manchester and Belfast
Speaker: Dr Krishna Gummadi
Machine (data-driven learning-based algorithmic) 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). Recently concerns have been raised about the potential for bias and unfairness in such algorithmic decisions. Against this background, this talk attempted to tackle the following foundational questions about man-machine decision making:
- How do machines learn to make biased or unfair decisions?
- How can we quantify (measure) and control (mitigate) bias or unfairness in machine decision making?
- Can machine decisions be engineered to help humans control (mitigate) bias or unfairness in their own decisions?
Krishna Gummadi is the head of the Networked Systems research group at the Max Planck Institute for Software Systems (MPI-SWS) and a professor at the University of Saarland in Germany. His research interests lie in understanding and building social computing systems. His current projects focus on enhancing fairness, accountability, transparency, and explainability of automated (particularly, data-driven and learning-based) decision making systems. His work has been recognized by numerous awards including the ACM SIGCOMM Test-of-Time. He also received an ERC Advanced Grant in 2017 to investigate "Foundations for Fair Social Computing".