arXiv:2508.14311cs.LGcs.AI2025-08
动态调整多个公平性目标权重，实现在线决策中的自适应公平优化
Online Learning with Multiple Fairness Regularizers via Graph-Structured Feedback
- 基于图结构反馈的强化学习框架，实时融合多维度公平性约束
- 在未知权重下实现公平性目标的自适应平衡，提升决策系统鲁棒性
- 适合需要持续优化公平性的推荐、信贷等实时决策场景
自动化决策系统中日益需要同时满足多种可能冲突的公平性度量。这些公平性目标的权重通常事先未知，可能随时间变化，在本研究中必须通过序列交互动态学习。本文在置信区间带状设置下解决此挑战，其中决策基于图结构反馈进行。
原文摘要 · Abstract (English)
There is an increasing need to enforce multiple, often competing, measures of fairness within automated decision systems. The appropriate weighting of these fairness objectives is typically unknown a priori, may change over time and, in our setting, must be learned adaptively through sequential interactions. In this work, we address this challenge in a bandit setting, where decisions are made with graph-structured feedback.
公平性在线学习图结构自适应
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