Colloquium Shimei Pan: Bias and Fairness in AI
By Shimei Pan, Information Systems Department of UMBC, Baltimore, USA
AI technologies are increasingly being used for making consequential decisions such as determining whether someone is hired, offered a loan, and granted parole. Unfortunately, there have been a wide range of recent discoveries of biased AI systems that exhibit prejudice against certain groups of people such as women or people of color. There is an increasing concern that vulnerable groups in our society could be harmed by biased AI systems.
In this talk, I will present some of our recent work on bias and fairness in AI such as fairness definitions/assessment and bias mitigation. As AI fairness is not a purely technical construct, having social implications, I will also present our work on human-fair AI interaction to demonstrate that an algorithmic solution itself is often insufficient to achieve its intended societal goals.
Dr. Shimei Pan is an Associate Professor in the Information Systems Department and the director of the text mining and social media analytics lab at University of Maryland, Baltimore County. Previously, she was a research scientist at IBM Watson Research Center in New York. Her research focuses on Natural Language Processing (NLP), fair AI, and Human-AI Interaction. Her current Fulbright Award in Germany is on cross-cultural analysis of social biases with large pre-trained language models. Dr. Pan received a Ph.D. in Computer Science from Columbia University.