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arXiv Machine Learning

Integrating Fairness and Explainability in a Multiple Instance Reinforcement Learning System

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.

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To Go Far, Go Together: Diverse Preferences Induce a Curriculum for Reward Optimization

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