Events Calendar

AI Seminar

Speaker: Yu-Ru Lin, Univ. of Pitt., Univ of Pitt

Talk Title: A Gateway to Trustworthy AI: Using Visual Analytics to Unmask Coincidental Correlations

Abstract: Join Zoom Meeting https://usc.zoom.us/s/99782858348?pwd=MnlSdGlTVWNETGFFbDQ4OWRmakdEQT09 Meeting ID: 997 8285 8348 Passcode: 580559 Register in advance for this webinar: https://usc.zoom.us/webinar/register/WN_xxYy3NkSQpidFYRY3fg_Ew In the realm of machine learning and data-driven decision-making, the risk of spurious and biased associations poses significant challenges to the integrity and reliability of AI systems. In this talk, I will introduce how visual analytic designs can empower data practitioners in navigating these complex issues. First, through a human-in-the-loop workflow, we tackle the problem of AI blindspots in classification models, where key patterns are often missed or misleading. Our design offers visually interpretable statistical methods to quantify and understand concept associations. It also includes debiasing techniques to address misleading patterns in data. Second, we tackle Simpson’s Paradox, a phenomenon where associations in data appear contradictory at different levels of aggregation, leading to cognitive confusion and incorrect interpretations. Our design offers an intuitive causal analysis framework and a human-centric workflow, enabling users to identify, understand, and prevent spurious associations, leading to more accountable causal decision-making. Together, these design frameworks contribute to making AI more trustworthy, offering robust tools for overcoming the challenges of spurious and biased associations in machine learning through advanced visual analytics.

Biography: Website: http://www.yurulin.com/  Yu-Ru Lin is an Associate Professor in the School of Computing and Information and the Research Director of the Institute for Cyber Law, Policy, and Security (Pitt Cyber) at the University of Pittsburgh, where she directs the PITT Computational Social Dynamics Lab (PICSO LAB). Her research lies at the intersection of Computational Social Science, Data Mining, and Visualization. She specializes in using social network and text data along with statistical learning tools and social theories to study phenomena spanning societal events and policy, anomalous behaviors, and other crucially important complex patterns concerning collective attention and actions, as well as human and social dynamics in response to societal risks. Her work has appeared in prestigious scientific venues and has been featured in the press, including WSJ, The Boston Globe, The Atlantic, MIT News, and NPR. She has authored or co-authored more than 100 refereed journal and conference papers and served on more than 50 conference program committees in the areas of big data, network science, and computational social science. She has served as a chair/co-chair of leading computational social science, web mining, and social media conferences such as AAAI ICWSM and TheWebConference/WWW (Web & Society Track). She currently serves as an Editor-in-Chief of AAAI ICWSM and an Associate Editor for multiple journals, including PLOS ONE,  Springer EPJ Data Science, Nature's Scientific Reports, and Frontiers in Big Data. She was selected as a Fellow of Kavli Frontiers of Science, National Academy of Sciences (NAS).

Host: Fred Morstatter and Zhuoyu Shi

More Info: https://www.isi.edu/events/4389/ai-seminar-a-gateway-to-trustworthy-ai-using-visual-analytics-to-unmask-coincidental-correlations/

Webcast: https://www.youtube.com/watch?v=2uZOOM6-noo

Location: Information Science Institute (ISI) - Virtual Only

WebCast Link: https://www.youtube.com/watch?v=2uZOOM6-noo

Audiences: Everyone Is Invited

Contact: Pete Zamar

Event Link: https://www.isi.edu/events/4389/ai-seminar-a-gateway-to-trustworthy-ai-using-visual-analytics-to-unmask-coincidental-correlations/