Chirag Shah, Professor
Information School
University of Washington

Abstract
Bias in data as well as lack of transparency and fairness in algorithms are not new problems, but with the increasing scale, complexity, and adoption, most AI systems are suffering from these issues at a level unprecedented. Information access systems are not spared since these days, almost all large-scale systems of information access are mediated by algorithms. These algorithms are optimized not only for relevance, which is subjective to begin with, but also for measures of engagement and impressions. They are picking up signals of what may be 'good' from individuals and perpetuating that through learning methods that are opaque and hard to debug. Considering 'fairness' and introducing more transparency can help, but it can also backfire or create other issues. We also need to understand how and why users of these systems engage with content. In this talk, I will share some of our attempts for bringing fairness in ranking systems and then talk about how the solutions are not that simple. To really address the problems of misinformation and misrepresentation in information access, we need to look for human-AI synergy where the responsibility of fairness and transparency lies not only on systems, but also on developers, regulators, and end-users.

Speaker Bio
Chirag Shah is Professor in Information School (iSchool) at University of Washington (UW) in Seattle. He is also Adjunct Professor with Paul G. Allen School of Computer Science & Engineering as well as Human Centered Design & Engineering (HCDE). He is the Founding Director for InfoSeeking Lab and Founding Co-Director of Center for Responsibility in AI Systems & Experiences (RAISE). His research interests include intelligent search and recommender systems, trying to understand the task a person is doing and providing proactive recommendations. In addition to creating task-based systems that provide more personalized reactive and proactive recommendations, he is also focusing on making such systems transparent, fair, and free of biases. He is the recipient of 2019 Microsoft BCS/BCS IRSG Karen Spärck Jones Award.