Fair Personalized Learner Modeling Without Sensitive Attributes

Hefei Xu, Min Hou, Le Wu, Fei Liu, Yonghui Yang, Haoyue Bai, Richang Hong, Meng Wang

Track: User modeling, personalization and recommendation

Keywords: Fairness, User Modeling, Cognitive Diagnosis

Abstract:

Personalized learner modeling uses learners' historical behavior data to diagnose their cognitive abilities, a process known as Cognitive Diagnosis (CD) in the literature.
This is a fundamental yet crucial task in web-based learning services, such as learning resource recommendation and adaptive testing.
Previously, researchers discovered that models improperly correlate learners' abilities with their sensitive attributes, resulting in unfair diagnoses for learners from different sensitive groups (e.g., gender, region).
Given the input of sensitive attributes, researchers proposed decorrelating these attributes from the modeling process, demonstrating improved fairness results.
However, privacy concerns make collecting sensitive attributes impractical. This challenge is compounded by the presence of multiple sensitive attributes, making fairness improvement under any of them difficult.
In this paper, we explore how to achieve fair personalized learner modeling without relying on any sensitive attribute input. Specifically, we first introduce a novel fairness objective tailored for personalized learner modeling. We then propose a max-min strategy that facilitates both potential sensitive information inference and fair CD modeling. In the max step, we propose a pseudo-label inference method based on maximizing the designed fairness objective. Given these pseudo-labels, the min step involves retraining a fair CD model by minimizing the designed objective.
Additionally, we provide a theoretical guarantee that implementing our proposed framework reduces the upper bound of fairness generalization error.
Extensive experiments demonstrate that
the proposed framework significantly outperforms existing methods in terms of fairness and accuracy. Our code is available at
https://anonymous.4open.science/r/FairWISA-40C6/.

Submission Number: 19