As Machine Learning systems become increasingly popular across diverse application domains, including those with direct human implications, the imperative of equity and algorithmic fairness has risen to prominence in the Artificial Intelligence community. On the other hand, in the context of Shared Micromobility Systems, the exploration of fairness-oriented approaches remains limited. Addressing this gap, we introduce a pioneering investigation into the balance between performance optimization and algorithmic fairness in the operation and control of Shared Micromobility Services. Our study leverages the Q-Learning algorithm in Reinforcement Learning, benefiting from its convergence guarantees to ensure the robustness of our proposed approach. Notably, our methodology stands out for its ability to achieve equitable outcomes, as measured by the Gini index, across different station categories--central, peripheral, and remote. Through strategic rebalancing of vehicle distribution, our approach aims to maximize operator performance while simultaneously upholding fairness principles for users. In addition to theoretical insights, we substantiate our findings with a case study or simulation based on synthetic data, validating the efficacy of our approach. This paper underscores the critical importance of fairness considerations in shaping control strategies for Shared Micromobility Services, offering a pragmatic framework for enhancing equity in urban transportation systems.

翻译：随着机器学习系统在包括直接影响人类生活的各个应用领域日益普及，公平性与算法公平的迫切性已在人工智能社区中凸显。然而，在共享微出行系统领域，针对公平性的方法探索仍十分有限。为填补这一空白，我们首次系统研究了共享微出行服务运行与控制中性能优化与算法公平之间的平衡问题。本研究采用强化学习中的Q-Learning算法，利用其收敛性保障确保所提方法的稳健性。值得注意的是，该方法能够依据基尼系数衡量标准，在中心站、周边站和偏远站等不同站点类别间实现公平结果。通过战略性重新平衡车辆分布，本方法旨在最大化运营商性能的同时，维护用户公平原则。除理论见解外，我们基于合成数据开展案例研究或仿真验证，实证了该方法的有效性。本文强调了在制定共享微出行服务控制策略时考虑公平性的关键重要性，并为提升城市交通系统的公平性提供了实用框架。