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Keynote Speakers

Prof. Chirag Shah
University of Washington, USA
Title Addressing Bias in Search and Fairness in Machine Learning
Abstract Bias is omnipresent — from data to algorithms, and from framing of a problem to interpreting its solution. In this talk, I will highlight how such bias in general with machine learning techniques, and in particular with search and recommender systems cause material problems for users, businesses, and society at large. The examples span areas of search, education, and health. I will then introduce the idea of marketplace as a way to find a balance or fairness in the system and address the issue of bias, among other things. I will draw specific examples from our work on search and recommendation systems to demonstrate that achieving fairness in a marketplace and addressing bias in data and algorithms are not just morally and ethically right things to do, but could also lead to a more sustainable growth for various industries, governments, and our scientific advancement.