Fair Classification via Transformer Neural Networks: Case Study of an Educational Domain
It addresses fairness concerns in educational ML applications, which is important for students and institutions, but is incremental as it applies existing transformer methods to new data.
The paper investigates fairness in transformer neural networks applied to tabular educational datasets, specifically Law School and Student-Mathematics, by evaluating trade-offs between fairness metrics and accuracy, with impressive results on the Law School dataset.
Educational technologies nowadays increasingly use data and Machine Learning (ML) models. This gives the students, instructors, and administrators support and insights for the optimum policy. However, it is well acknowledged that ML models are subject to bias, which raises concerns about the fairness, bias, and discrimination of using these automated ML algorithms in education and its unintended and unforeseen negative consequences. The contribution of bias during the decision-making comes from datasets used for training ML models and the model architecture. This paper presents a preliminary investigation of the fairness of transformer neural networks on the two tabular datasets: Law School and Student-Mathematics. In contrast to classical ML models, the transformer-based models transform these tabular datasets into a richer representation while solving the classification task. We use different fairness metrics for evaluation and check the trade-off between fairness and accuracy of the transformer-based models over the tabular datasets. Empirically, our approach shows impressive results regarding the trade-off between fairness and performance on the Law School dataset.