提升教育AI模型在多敏感属性下的公平性，效果优于现有方法。
FAIREDU: A Multiple Regression-Based Method for Enhancing Fairness in Machine Learning Models for Educational Applications
- 基于多元回归建模，同时处理性别、种族、年龄等多重敏感特征的公平性问题。
- 在多个教育数据集上验证，显著改善公平性，对模型准确率影响极小。
- 适合关注教育公平的AI研究者与应用开发者使用。
人工智能与机器学习（AI/ML）模型的公平性日益重要，尤其当这些系统决策影响不同群体时。在教育这一全球关键领域，AI/ML系统的广泛应用引发了对公平性的特别关注。当前研究多集中于单一敏感特征的公平性，难以全面评估。本文提出FAIREDU，一种新颖有效的多敏感特征公平性增强方法。通过大量实验，验证了其在不牺牲模型性能的前提下显著提升公平性。结果表明，该方法能有效应对性别、种族、年龄等多重敏感特征的交叉不公平问题，优于现有最先进方法，且对模型准确率影响微小。论文还探讨了未来研究方向，以进一步提升方法的鲁棒性和在多种模型与数据集上的适用性。
原文摘要 · Abstract (English)
Fairness in artificial intelligence and machine learning (AI/ML) models is becoming critically important, especially as decisions made by these systems impact diverse groups. In education, a vital sector for all countries, the widespread application of AI/ML systems raises specific concerns regarding fairness. Current research predominantly focuses on fairness for individual sensitive features, which limits the comprehensiveness of fairness assessments. This paper introduces FAIREDU, a novel and effective method designed to improve fairness across multiple sensitive features. Through extensive experiments, we evaluate FAIREDU effectiveness in enhancing fairness without compromising model performance. The results demonstrate that FAIREDU addresses intersectionality across features such as gender, race, age, and other sensitive features, outperforming state-of-the-art methods with minimal effect on model accuracy. The paper also explores potential future research directions to enhance further the method robustness and applicability to various machine-learning models and datasets.
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