Abstract: 

Machine learning models are increasingly used to inform high-stakes decisions about people. Discrimination in machine learning becomes objectionable when it places certain privileged groups at the systematic advantage and certain unprivileged groups at the systematic disadvantage. We have developed the AI Fairness 360 (AIF360), a comprehensive Python package (https://github.com/ibm/aif360) that contains nine different algorithms, developed by the broader algorithmic fairness research community, to mitigate that unwanted bias. AIF360 also provides an interactive experience (http://aif360.mybluemix.net/data) as a gentle introduction to the capabilities of the toolkit for people unfamiliar with Python programming. Compared to existing open source efforts on AI fairness, AIF360 takes a step forward in that it focuses on bias mitigation (as well as bias checking), industrial usability, and software engineering. In our proposed hands-on tutorial, we will teach participants to use and contribute to AIF360 enabling them to become some of the first members of the community. Toward this goal, all participants in this tutorial will get to experience first-hand: 1) how to use the metrics provided in the toolkit to check the fairness of an AI application, and 2) how to mitigate bias they discover. Our goal in creating a vibrant community, centered around the toolkit and its application, is to contribute to efforts to engender trust in AI and make the world more equitable for all.

Bio: 

Kush R. Varshney was born in Syracuse, NY in 1982. He received the B.S. degree (magna cum laude) in electrical and computer engineering with honors from Cornell University, Ithaca, NY, in 2004. He received an S.M. degree in 2006 and a Ph.D. degree in 2010, both in electrical engineering and computer science from the Massachusetts Institute of Technology (MIT), Cambridge. While at MIT, he was a National Science Foundation Graduate Research Fellow.
Dr. Varshney is a principal research staff member and manager with IBM Research AI at the Thomas J. Watson Research Center, Yorktown Heights, NY. He is the founding co-director of the IBM Science for Social Good initiative. He applies data science and predictive analytics to human capital management, healthcare, olfaction, computational creativity, public affairs, international development, and algorithmic fairness, which has led to recognitions such as the 2013 Gerstner Award for Client Excellence for contributions to the WellPoint team and the Extraordinary IBM Research Technical Accomplishment for contributions to workforce innovation and enterprise transformation. He conducts academic research on the theory and methods of statistical signal processing and machine learning. His work has been recognized through best paper awards at the Fusion 2009, SOLI 2013, KDD 2014, and SDM 2015 conferences. He is a senior member of the IEEE and a member of the Partnership on AI's Safety-Critical AI working group.