FAIREDU：一种基于多元回归的增强教育应用机器学习模型公平性的方法
FAIREDU: A Multiple Regression-Based Method for Enhancing Fairness in Machine Learning Models for Educational Applications
AI总结：
针对教育领域AI/ML模型公平性研究多聚焦单敏感特征、评估不全面的问题，提出基于多元回归的FAIREDU方法，可兼顾多敏感特征交叉公平性，在几乎不影响准确率的前提下优于现有最优方法。
摘要：
人工智能与机器学习（AI/ML）模型的公平性正变得至关重要，尤其是当这些系统做出的决策会影响不同群体时。教育是各国的重要领域，AI/ML系统在该领域的广泛应用引发了关于公平性的特定担忧。当前研究主要聚焦于单个敏感特征的公平性，这限制了公平性评估的全面性。本文提出了FAIREDU，这是一种新颖且有效的方法，旨在提升跨多个敏感特征的公平性。通过大量实验，我们评估了FAIREDU在不损害模型性能的前提下增强公平性的效果。结果表明，FAIREDU能够解决性别、种族、年龄等敏感特征之间的交叉性问题，性能优于现有最优方法，且对模型准确率的影响极小。本文还探讨了未来潜在的研究方向，以进一步提升该方法的鲁棒性，以及其在各类机器学习模型和数据集上的适用性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
