Striking a Balance in Fairness for Dynamic Systems Through Reinforcement Learning
Striking a Balance in Fairness for Dynamic Systems Through Reinforcement Learning
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DOI：
10.1109/bigdata59044.2023.10386299
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发表时间：
2023-12
期刊：
影响因子：
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通讯作者：
Yaowei Hu;Jacob Lear;Lu Zhang
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文献类型：
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作者：
Yaowei Hu;Jacob Lear;Lu Zhang
虽然在公平机器学习领域已经取得了重大进展，但大多数研究都集中在决策模型在静态总体上运行的场景。在这篇文章中，我们研究了动态系统中的公平性，其中序列决策被做出。每一项决策都可能改变功能或用户行为的基本分布。通过马尔可夫决策过程(MDP)对动态系统进行建模。通过承认传统公平概念和长期公平是不同的要求，不一定彼此一致，我们提出了一个算法框架，将各种公平考虑与使用预处理和处理中的强化学习相结合。三个案例研究表明，我们的方法可以在传统公平观念、长期公平和效用之间取得平衡。
While significant advancements have been made in the field of fair machine learning, the majority of studies focus on scenarios where the decision model operates on a static population. In this paper, we study fairness in dynamic systems where sequential decisions are made. Each decision may shift the underlying distribution of features or user behavior. We model the dynamic system through a Markov Decision Process (MDP). By acknowledging that traditional fairness notions and long-term fairness are distinct requirements that may not necessarily align with one another, we propose an algorithmic framework to integrate various fairness considerations with reinforcement learning using both pre-processing and in-processing approaches. Three case studies show that our method can strike a balance between traditional fairness notions, long-term fairness, and utility.