News & Event

Seminar

News & Event

Seminar

Fair Machine Learning for Education-An Information Theorist’s Perspective

Subject

Fair Machine Learning for Education-An Information Theorist's Perspective

Date

November 15 (Tuesday) 1PM

Speaker

Prof. Haewon Jeong, University of California Santa Barbara

Place

N1, Room 111

Overview:

O Speaker: Prof. Haewon Jeong, University of California Santa Barbara

O Title: Fair Machine Learning for Education-An Information Theorist’s Perspective

O Date:  November 15 (Tuesday) 

O Start Time: 1PM

O Venue: N1, Room 111

OAbstract:

Is it a good idea to use machine learning (ML) predictions in education? Would machine learning models treat all students fairly? I will start this talk with our recent analysis on middle school and high school datasets that reveal potential fairness risks of applying vanilla ML on students. To improve fairness in ML for education, there are several practical challenges. First, there are missing values in the datasets that are not evenly distributed across groups (e.g., female and male) which could aggravate the ML model’s bias. I will show a fundamental limit of learning with missing values and propose a decision-tree-based algorithm that outperforms state-of-the-art fair ML methods that do not consider missing values. In the second part, we address how to correct bias in a classifier with low-cost post-processing when we have multi-class labels and sensitive attributes. I will introduce the Fair Projection algorithm which utilizes the idea of “information projection” and how it can be applied to a wide range of classifiers while maintaining a competitive fairness-accuracy trade-off. 

 

O Bio:

Haewon Jeong is an assistant professor of Electrical and Computer Engineering at the University of California Santa Barbara. She received the B.S. degree (’14) in Electrical Engineering from KAIST and the M.Sc. (’16) and Ph.D. (’20) degrees in Electrical and Computer Engineering from Carnegie Mellon University. From 2020 to 2022, she was a postdoctoral scholar at Harvard University. Her research interests include information theory, distributed computing, machine learning, and ethics of AI systems. 
 
 

 

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