As decision‐making increasingly relies on machine learning (ML) and (big) data, the issue
of fairness in data‐driven artificial intelligence systems is receiving increasing attention from …

In this paper, we review algorithmic bias in education, discussing the causes of that bias and
reviewing the empirical literature on the specific ways that algorithmic bias is known to have …

Predictive modeling is a core technique used in tackling various tasks in learning analytics
research, eg, classifying educational forum posts, predicting learning performance, and …

In the near future, systems, that use Artificial Intelligence (AI) methods, such as machine
learning, are required to be certified or audited for fairness if used in ethically sensitive fields …

Automatic classifiers of educational forum posts are essential in helping instructors
effectively implement their teaching practices and thus have been widely investigated …

Machine Learning (ML) techniques have been increasingly adopted to support various
activities in education, including being applied in important contexts such as college …

Abstract Pretrained Language Models (PLMs), though popular, have been diagnosed to
encode bias against protected groups in the representations they learn, which may harm the …

Data-driven technologies for education, such as artificial intelligence in education (AIEd)
systems, learning analytics dashboards, open learner models, and other applications, are …

Educational researchers have examined artificial intelligence (AI) to automatically support
students' learning at a large scale. However, it has been broadly identified that AI models …

Predictive analytics are increasingly pervasive in higher education. However, algorithmic
bias has the potential to reinforce racial inequities in postsecondary success. We provide a …