  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.
