2023 AAHSL Annual Meeting
November 3 | Seattle, WA

About the Matheson Lecture Program

AI Hopes, Hypes, and Horrors: How Do We Do this Right?

Over the last several years, AI-driven informational systems have proliferated around us. The pace at which AI is getting integrated in search, recommendation, and decision-support systems has quickened recently. Many of these are built on hopes and hypes of what AI could deliver, but it is not always clear when or if a real value to individuals and society is provided that justifies the risks we take in building and delivering such solutions. These systems are often mired in bias and unfairness (to individuals or groups), lack transparency and accountability, and provide information that are fabricated and even toxic. We are at a crossroad where we have important choices to make that will impact many aspects of our lives in profound ways.

Can we overcome the challenges posed by applying AI solutions and leverage their potential for all of us? Can we mitigate risks and harms enough to bring the reward-to-risk ratio high enough for various areas like education, healthcare, and information access? Can we do this right? In this talk, I will bring forth some of these recent opportunities and challenges with AI in order to highlight where and why we are seeing the issues that hinder us from getting real benefits of AI at the societal level. My examples will come from accessibility, education, healthcare, and policy. I will then argue that we need advancements not just at technical levels, but also at policy, societal, and even philosophical levels. I may not have solutions, but I want to leave with the right or better questions for us to ask in this space

About the Presenter

Chirag Shah, PhD 

Chirag Shah is Professor of Information and Computer Science at University of Washington (UW) in Seattle. He is the Founding Director for InfoSeeking Lab and Founding Co-Director of Center for Responsibility in AI Systems & Experiences (RAISE). He works in the area of intelligent information access systems, focusing on task-oriented search, proactive recommendations, and conversational systems. He is also engaged in work with generative AI, specifically in information access using large language models (LLMs). In addition to creating AI-driven information access systems that provide more personalized reactive and proactive recommendations, he is also focusing on making such systems transparent, fair, and free of biases.

Shah is a Distinguished Member of Association of Computing Machinery (ACM) as well as Association for Information Science & Technology (ASIS&T). He has published nearly 200 peer-reviewed articles and authored seven books, including text books on data science and machine learning. He also works closely with industrial research labs on cutting-edge problems, typically as a visiting researcher. The most recent engagements included Amazon, Getty Images, Microsoft Research, and Spotify.