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

Assistant Professor Na Zou, University of Houston
Room S105

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
focus on the current progress of her research, specifically addressing fairness
at three key stages in AI/ML: enhancing data quality, refining algorithmic
design, and optimizing model deployment. The talk will be concluded by the
future research plan.

About the speaker
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 venues such
as Technometrics, IISE Transactions and ACM Transactions, including one
Best Paper Finalist and one Best Student Paper Finalist at INFORMS QSR
section, two featured articles at ISE Magazine and one student innovation
award at AMIA annual symposium. She was the recipient of IEEE Irv
Kaufman Award, Texas A&M Institute of Data Science Career Initiation
Fellow and NSF CAREER Award.