AI 中文总结
针对不可靠建议损害在线分配效率与公平性的问题，提出结合建议、保守后备与公平修正的鲁棒规则，实验验证其稳定性并显著降低曝光差距。
AI 中文摘要
学习增强算法利用预测改进在线决策，但不可靠建议可能损害效率与公平性。我们研究具有有限候选集、不可逆决策及曝光约束的在线分配问题，提出结合建议、保守后备机制与公平性修正的鲁棒公平规则。在有界误差假设下，我们证明其一致性与鲁棒性，损失与预测误差成比例。实验表明其在对抗性建议下具有稳定性，且曝光差距显著降低。
英文摘要
Learning-augmented algorithms improve online decisions using predictions, but unreliable advice may harm efficiency and fairness. We study an online allocation problem with finite candidate sets, irreversible decisions, and exposure constraints. We propose a robust and fair rule combining advice with a conservative fallback and fairness correction. Under bounded-error assumptions, we prove consistency and robustness with loss proportional to prediction error. Experiments show stability under adversarial advice and significant reductions in exposure disparity.
