论文标题
通过变压器神经网络进行公平分类：教育领域的案例研究
Fair Classification via Transformer Neural Networks: Case Study of an Educational Domain
论文作者
论文摘要
如今，教育技术越来越多地使用数据和机器学习（ML）模型。这为学生，讲师和管理员提供了最佳政策的支持和见解。但是，众所周知，ML模型会受到偏见的影响，这引起了人们对在教育中使用这些自动化的ML算法的公平，偏见和歧视的关注，以及其意外且不可预见的负面后果。决策过程中偏见的贡献来自用于培训ML模型和模型体系结构的数据集。本文对两个表格数据集上的变压器神经网络的公平性进行了初步调查：法学院和学生数学。与经典ML模型相反，基于变压器的模型在求解分类任务时将这些表格数据集转换为更丰富的表示。我们使用不同的公平指标来评估并检查表格数据集中基于变压器的模型的公平性和准确性之间的权衡。从经验上讲，我们的方法在法学院数据集中的公平与绩效之间的权衡显示了令人印象深刻的结果。
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.
