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: