CIIR Talk Series: Chirag Shah

Speaker: Chirag Shah, University of Washington

Talk Title: Can Large Scale Search Ever Be Fair, Unbiased, and Transparent?

Date: Friday. November 5, 2021 - 1:30 - 2:30 PM EDT (North American Eastern Daylight Saving Time) via Zoom

Zoom Access: Zoom Link and reach out to Alex Taubman for the passcode.

Abstract: The core element for a good search system is relevance. This notion has been challenged by many in the research community as scholars have pushed us to think beyond objective relevance to incorporate situational and contextual relevance, as well as measures of usefulness and utility. Large scale search systems, which are often commercially-run can have even more measures related to their business that they need to optimize. All of these lead to search systems that have become more opaque and more disconnected from what may be really relevant, useful, or good for a user. In this talk, I will argue that these business objectives of large scale search systems are often in conflict with what may be good for an individual or the society at large. The costs of this conflict are many -- from misinformation to loss of agency for an end user. Can we really have a search or a recommender system that can keep various stakeholders happy? How do we think beyond objective relevance and business utilities to create a fairer, more sustainable future for search systems? I will invite you to co-construct the answers to this with me and envision a new era for IR.

Bio: Chirag Shah is an Associate Professor in Information School (iSchool) at University of Washington (UW) in Seattle. He is also an Adjunct Associate 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 Responsible 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 spent his sabbatical in 2018 at Spotify working on voice-based search and recommendation problems. In 2019, as an Amazon Scholar, he worked with Amazon’s Personalization team on applications involving personalized and task-oriented recommendations. In 2020, he was a Visiting Researcher at MSR AI, working on creating intelligent task management in search and productivity apps. More recently, he has been working with Getty Images to build task-focused search with relevance and diversity constructs. He is the recipient of Microsoft BCS/BCS IRSG Karen Spärck Jones Award 2019.