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arXiv Machine Learning By Bente Hinkenhuis, Seyed Sahand Mohammadi Ziabari, Ali Mohammed Mansoor Alsahag

Integrating Fairness and Explainability in a Multiple Instance Reinforcement Learning System

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The paper proposes a multi‑objective framework that integrates reinforcement learning‑based multiple instance learning (RL‑MIL), adversarial debiasing, and preference‑conditioned hypernetworks to predict student performance while addressing fairness concerns. Each student is represented as a bag of weakly labeled interactions, and an RL agent selects informative instances for classification. Although the baseline RL‑MIL achieves strong accuracy, the hypernetwork extensions fail to produce a smooth trade‑off between predictive performance and Equalized Odds due to mode collapse, objective dominance, and weak gradient propagation, indicating that additional mechanisms are needed for stable, fair RL‑MIL systems.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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