Counterfactual Fairness Evaluation of Machine Learning Models on Educational Datasets

This paper investigates counterfactual fairness—a causal approach to algorithmic fairness—in machine learning models applied to educational datasets, examining whether decisions for students would remain the same if their demographic attributes (race, gender) were different. The work evaluates fairness and bias in predictive models used for academic success prediction, at-risk detection, and other educational decision-making contexts.

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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