Join us for an AI + Health Seminar with Na Zou, Assistant Professor, University of Houston.

When: April 10 (noon-12:30PM) 
Zoom:  https://utexas.zoom.us/j/5128555388

Title: Exploring and Exploiting Fairness in AI/ML: Algorithms and Applications

Abstract: AI/ML algorithms have made significant advancements and are extensively used in critical applications such as employment, personalized medicine, and more. Despite the success, ensuring fairness in AI/ML remains a significant challenge. These algorithms may inadvertently perpetuate or even magnify biases in the data, resulting in discriminatory outcomes against specific groups or individuals. This issue hinders the widespread adoption of AI/ML in high-stakes applications.

This talk will explore the concept of fairness in AI/ML from a computational perspective, encompassing the measurement, detection, and mitigation of unfairness to address diverse challenges throughout the AI/ML life cycle. The speaker will first introduce real-world examples, fundamental concepts and the existing work. The speaker will also highlight her ongoing research, specifically addressing fairness at three key stages in AI/ML: enhancing data quality, refining algorithmic design, and optimizing model deployment. The talk will conclude with a case study on organ transplant.

Speaker Bio: Dr. Na Zou is an Assistant Professor in Industrial Engineering at University of Houston. Her research is to develop effective, efficient and fair AI/ML algorithms for tackling data challenges raised by large-scale, dynamic and networked data from various real-world information systems. Specifically, Dr. Zou's research focuses on fairness in machine learning,

interpretable machine learning, transfer learning, and network modeling and inference. The research projects have resulted in publications at prestigious Journals such as Technometrics, IISE Transactions, ACM Transactions, and IEEE Transactions, also at top Machine Learning Conferences such as ICLR, ICML and NeurIPS.  In the publications, there includes Best Paper Finalists, Best Student Paper Finalists and Best Paper Awards at INFORMS, ICQSR, AMIA. Her work has been featured twice at ISE Magazine and received one student innovation award at AMIA. She was the recipient of IEEE Irv Kaufman Award, Texas A&M Institute of Data Science Career Initiation Fellow and NSF CAREER Award.