Towards Equity and Algorithmic Fairness in Student Grade Prediction
Project Overview
The document explores the transformative role of generative AI in education, highlighting its potential to enhance learning outcomes while addressing critical issues of equity and fairness. It focuses on the application of AI in grade prediction within higher education, emphasizing methodologies designed to mitigate racial disparities in algorithm performance. Techniques such as adversarial learning and data balancing are discussed as effective strategies for ensuring that AI systems produce equitable results. The findings underscore the necessity for educational institutions to adopt AI technologies that prioritize fairness, thereby preventing the reinforcement of existing biases. Ultimately, the document advocates for the responsible and equitable deployment of generative AI in educational settings to ensure that all students benefit from advancements in technology without facing discrimination or inequity.
Key Applications
Grade prediction models using AI
Context: Higher education institutions aiming to improve student outcomes and support historically underserved groups.
Implementation: Developed methodologies for grade prediction that include label balancing and adversarial learning to minimize bias.
Outcomes: Improved equity in grade prediction accuracy among diverse racial groups and enhanced overall predictive performance.
Challenges: Need to balance the trade-off between overall accuracy and fairness; potential for perpetuating biases if not implemented carefully.
Implementation Barriers
Bias and fairness barrier
Machine learning models may replicate and amplify biases present in historical data, leading to challenges in achieving both high accuracy and fairness in predictive models.
Proposed Solutions: Implement data processing techniques such as adversarial learning and sample weighting to mitigate algorithmic bias. Additionally, utilize strategies for group fairness evaluation and equitable sampling during model training.
Project Team
Weijie Jiang
Researcher
Zachary A. Pardos
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Weijie Jiang, Zachary A. Pardos
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18
Analysis Provider: Openai