  In education data mining (EDM) communities, machine learning has achieved
remarkable success in discovering patterns and structures to tackle educational
challenges. Notably, fairness and algorithmic bias have gained attention in
learning analytics of EDM. With the increasing demand for the right to be
forgotten, there is a growing need for machine learning models to forget
sensitive data and its impact, particularly within the realm of EDM. The
paradigm of selective forgetting, also known as machine unlearning, has been
extensively studied to address this need by eliminating the influence of
specific data from a pre-trained model without complete retraining. However,
existing research assumes that interactive data removal operations are
conducted in secure and reliable environments, neglecting potential malicious
unlearning requests to undermine the fairness of machine learning systems. In
this paper, we introduce a novel class of selective forgetting attacks designed
to compromise the fairness of learning models while maintaining their
predictive accuracy, thereby preventing the model owner from detecting the
degradation in model performance. Additionally, we propose an innovative
optimization framework for selective forgetting attacks, capable of generating
malicious unlearning requests across various attack scenarios. We validate the
effectiveness of our proposed selective forgetting attacks on fairness through
extensive experiments using diverse EDM datasets.
