Dr. Christopher W. Clifton
Professor of Computer Science
Purdue University
Title: Don’t be a Headline: Fairness and Transparency in Machine Learning
Track: Machine Learning/ Academia
Dealing with Discriminatory Data Mining
There is growing evidence that algorithms running on “Big Data” can lead to outcomes that are biased against underrepresented groups. This is in spite of the fact that such group information (race, gender, religion, etc.) is not used by the algorithms.
This talk will discuss some of the issues, pointing out evidence, and hypothesize causes. We will then look at one solution, based on adapting a Bayesian network classifier to reduce disparate impact on groups that are treated “differently” by the originally learned classifier.
This talk is based on work with Koray Mancuhan that appeared in Artificial Intelligence and Law (2014) 22:211-238.
Bio:
Dr. Clifton is a Professor of Computer Science at Purdue University. He works on data privacy, particularly with respect to analysis of private data. From 2013-2016, Dr. Clifton served as a program director at the National Science Foundation. Prior to joining Purdue in 2001, he was a principal scientist in the Information Technology Division at the MITRE Corporation. Before joining MITRE in 1995, he was an assistant professor of computer science at Northwestern University.
