Counterfactual Fairness Evaluation of Machine Learning Models on Educational Datasets

This paper evaluates counterfactual fairness (a causal individual-level fairness notion) of machine learning models on educational datasets, examining how sensitive attributes like race and gender causally influence predictions of student outcomes. The study demonstrates counterfactual fairness analysis on benchmark educational datasets to assess whether models produce the same decisions regardless of demographic group membership.

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 conduct

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