顺序性决策中的算法回顾对于长期的公平性
1Department of Electrical Engineering and Computer Science, University of Arkansas, Fayetteville, AR, United States.
Frontiers in big data
|February 20, 2026
概括
在连续的决策中实现长期公平是很困难的. 本研究介绍了顺序因果算法诉求公平 (SCARF) 以创建个性化计划,以减少随时间的差异.
科学领域:
- 人工智能的人工智能
- 计算机科学 计算机科学
- 机器学习 机器学习
背景情况:
- 顺序性决策对长期的公平性构成挑战,因为早期的选择可能会加剧差异.
- 当前的公平性方法往往直接调整决策模型,忽视个人干预.
研究的目的:
- 通过顺序算法诉讼来引入一个新的框架,以实现长期公平.
- 开发一种基于因果关系的方法,用于生成个性化,时间连贯的补救计划.
主要方法:
- 引入了对公平性的顺序因果算法诉讼 (SCARF).
- 综合结构因果建模与顺序生成建模.
- 内置的短期和长期公平约束与预算限制.
主要成果:
- 在多个决策周期中,SCARF生成个性化的诉求轨迹,减轻多个决策周期中的差异.
- 经验分析显示,在连续干预下,短期和长期公平性之间存在权衡.
- 在合成和半合成数据集上证明了SCARF的有效性.
结论:
- 在动态决策中,SCARF为分析和改善长期公平性提供了一个实用的框架.
- 该方法为个人提供可行的干预措施,以确保公平的结果.
- 强调在公平性干预中考虑时间动态的重要性.
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