用反事实公平性分析教育模型，揭示敏感属性的因果影响。
Counterfactual Fairness Evaluation of Machine Learning Models on Educational Datasets
- 基于反事实框架评估模型对敏感属性的因果公平性
- 在基准教育数据集上发现模型存在显著因果偏差
- 适合关注教育公平性的研究者与政策制定者
随着机器学习模型在教育领域的广泛应用，从识别风险学生到预测学业表现，算法偏见及其对学生的影响引发了对算法公平性的重大关切。尽管群体公平性在教育中被广泛研究，但基于因果关系的个体公平性，特别是反事实公平性，仍研究不足。本文通过在基准教育数据集上对机器学习模型进行反事实公平性分析，探索了反事实公平性在教育数据中的应用。结果表明，反事实公平性能够为敏感属性的因果机制及基于因果的个体公平性提供有意义的洞见。
原文摘要 · Abstract (English)
As machine learning models are increasingly used in educational settings, from detecting at-risk students to predicting student performance, algorithmic bias and its potential impacts on students raise critical concerns about algorithmic fairness. Although group fairness is widely explored in education, works on individual fairness in a causal context are understudied, especially on counterfactual fairness. This paper explores the notion of counterfactual fairness for educational data by conducting counterfactual fairness analysis of machine learning models on benchmark educational datasets. We demonstrate that counterfactual fairness provides meaningful insight into the causality of sensitive attributes and causal-based individual fairness in education.
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