Fairness, accountability and transparency in automated decision-making

Speaker:        Dr. Suresh Venkatasubramanian
                University of Utah

Title:          "Fairness, accountability and transparency in automated

Date:           Thursday, 28 September 2017

Time:           11:00am to 12 noon

Venue:          Room 4504 (via lift no. 25/26), HKUST


In the last few years we've seen the rapid rise of automated decision
making systems in all areas that touch our lives, whether it be hiring,
credit scoring, university admissions, medical diagnosis and the entire
pipeline of criminal justice. We've begun to tease out the technical
problems arising from the deployment of such systems, focused around
issues of fairness, accountability and transparency (FAT).

In this talk I'll take a look back and a peek forward. I'll outline the
research problems at the core of this new discipline, both in the core
area of machine learning and beyond to other areas in CS, and present what
we've learnt so far. I'll also present a vision for the next set of
challenges on the technical side and beyond.


Suresh Venkatasubramanian is an associate professor at the University of
Utah. His background is in algorithms and computational geometry, as well
as data mining and machine learning. His current research interests lie in
algorithmic fairness, and more generally the problem of understanding and
explaining the results of black box decision procedures. Suresh was the
John and Marva Warnock Assistant Professor at the U, and has received a
CAREER award from the NSF for his work in the geometry of probability, as
well as a test-of-time award at ICDE 2017 for his work in privacy. His
research on algorithmic fairness has received press coverage including
NPR's Science Friday, as well as in other media outlets. He is a member of
the board of the ACLU in Utah, and is a member of New York City's Failure
to Appear Tool (FTA) Research Advisory Council.