Time for a Paradigm Shift in Predictive Analytics: Debiasing Models with Fair AI

• In Event: D&D- Digital Learning Innovations (v)

Short Description

To support online learners at a large scale, educational researchers have adopted artificial intelligence (AI) techniques such as machine learning (ML) to predict their learning outcomes automatically. However, limited attention has been paid to the fairness of prediction with AI in educational settings. This study aims to fill the gap by introducing a generic algorithm that can work with existing AI algorithms while yielding fairer results.

Authors

• Presenter: Chenglu Li, University of Florida

• Contributor: Wanli Xing, University of Florida

• Contributor: Walter Leite, University of Florida