Exploring Fairness in Educational Data Mining in the Context of the Right to be Forgotten

TL;DR

This paper investigates how malicious selective forgetting attacks can undermine fairness in educational data mining models, proposing new attack methods and validating their effectiveness through experiments.

Contribution

It introduces a novel class of selective forgetting attacks targeting fairness in EDM models and an optimization framework to generate such attacks across different scenarios.

Findings

Selective forgetting attacks can compromise fairness without affecting accuracy.

Proposed attacks are effective across diverse EDM datasets.

Models remain unaware of fairness degradation caused by attacks.

Abstract

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.