Summary of Exploring Fairness in Educational Data Mining in the Context Of the Right to Be Forgotten, by Wei Qian et al.
Exploring Fairness in Educational Data Mining in the Context of the Right to be Forgotten
by Wei Qian, Aobo Chen, Chenxu Zhao, Yangyi Li, Mengdi Huai
First submitted to arxiv on: 27 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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Summary difficulty | Written by | Summary |
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High | Paper authors | High Difficulty Summary Read the original abstract here |
Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel class of selective forgetting attacks is introduced to compromise the fairness of learning models while maintaining their predictive accuracy, allowing malicious actors to undermine the model owner’s ability to detect performance degradation. This attack framework can be applied across various scenarios and demonstrates effectiveness on fairness through extensive experiments using diverse EDM datasets. |
Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning models are being used in education data mining (EDM) communities to discover patterns and structures that help tackle educational challenges. But there is a growing need for these models to forget sensitive data, particularly within the realm of EDM. Researchers have been working on machine unlearning, which eliminates the influence of specific data from a pre-trained model without complete retraining. However, this approach assumes that interactive data removal operations are conducted in secure and reliable environments. This paper introduces new attacks designed to compromise the fairness of learning models while maintaining their predictive accuracy, making it difficult for the model owner to detect performance degradation. |
Keywords
» Artificial intelligence » Machine learning