Fairness-aware learning aims at satisfying various fairness constraints in addition to the usual performance criteria via data-driven machine learning techniques. Most of the research in fairness-aware learning employs the setting of fair-supervised learning. However, many dynamic real-world applications can be better modeled using sequential decision-making problems and fair reinforcement learning provides a more suitable alternative for addressing these problems. In this article, we provide an extensive overview of fairness approaches that have been implemented via a reinforcement learning (RL) framework. We discuss various practical applications in which RL methods have been applied to achieve a fair solution with high accuracy. We further include various facets of the theory of fair reinforcement learning, organizing them into single-agent RL, multi-agent RL, long-term fairness via RL, and offline learning. Moreover, we highlight a few major issues to explore in order to advance the field of fair-RL, namely - i) correcting societal biases, ii) feasibility of group fairness or individual fairness, and iii) explainability in RL. Our work is beneficial for both researchers and practitioners as we discuss articles providing mathematical guarantees as well as articles with empirical studies on real-world problems.
翻译:公平认知学习除了通过数据驱动的机器学习技术,还旨在满足通常的绩效标准之外各种公平性限制; 公平认知学习的大多数研究都采用公平监督的学习环境; 然而,许多动态现实世界应用可以采用顺序决策问题和公平强化学习更好地模型化,为解决这些问题提供了一个更合适的替代方法; 在本条中,我们对通过强化学习框架(RL)实施的各种公平做法进行了广泛的概述; 我们讨论了各种实际应用,在这些应用中运用了RL方法,以便实现一个高度精确的公平解决方案; 我们还纳入了公平强化学习理论的各个方面,将公平强化学习组织成单一试剂RL、多试剂RL、通过RL的长期公平以及离线学习。 此外,我们强调要探索的一些重要问题,以推进公平学习领域,即:一)纠正社会偏见,二)群体公平或个人公平的可行性,以及三)在RL中解释。 我们的工作对研究人员和从业人员都有益,因为我们讨论了提供数学保障的文章,以及实际问题的经验研究。