FAIREDU: A Multiple Regression-Based Method for Enhancing Fairness in Machine Learning Models for Educational Applications
Project Overview
The document discusses the integration of generative AI in education, highlighting its transformative potential and key applications. One significant focus is FAIREDU, an innovative approach aimed at enhancing fairness in machine learning models utilized within educational contexts. This method specifically addresses fairness across multiple sensitive attributes, including gender, race, and age, demonstrating superior performance compared to existing fairness-enhancing techniques while sustaining model efficacy. The findings underscore the critical need for comprehensive assessments of fairness across diverse sensitive features, revealing challenges related to balancing fairness and model performance in educational datasets. Overall, the document illustrates how generative AI can be harnessed to create more equitable educational experiences, while also acknowledging the complexities involved in implementing these advanced technologies responsibly and effectively.
Key Applications
FAIREDU - A multiple regression-based method for enhancing fairness
Context: Educational applications involving machine learning models that impact diverse groups
Implementation: FAIREDU detects dependencies between sensitive and non-sensitive features using multivariate regression, removing biases before model training.
Outcomes: Improved fairness across multiple sensitive features without significantly compromising model performance.
Challenges: The method may not capture non-linear relationships inherent in datasets and may overlook other aspects of fairness.
Implementation Barriers
Technical barrier
FAIREDU relies on linear regression, which may not effectively address non-linear relationships in data, potentially leaving biases unaddressed.
Proposed Solutions: Future research should explore the use of non-linear methods and composite sensitive features.
External validity barrier
The datasets used for evaluation may not represent the full diversity of real-world educational environments, limiting generalizability.
Proposed Solutions: Testing FAIREDU across a broader range of datasets and educational contexts.
Construct validity barrier
Complex interactions between intersectional identities may not be fully captured by FAIREDU.
Proposed Solutions: Future work should address the model's ability to handle a broader array of sensitive features and interactions.
Project Team
Nga Pham
Researcher
Minh Kha Do
Researcher
Tran Vu Dai
Researcher
Pham Ngoc Hung
Researcher
Anh Nguyen-Duc
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Nga Pham, Minh Kha Do, Tran Vu Dai, Pham Ngoc Hung, Anh Nguyen-Duc
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