Recent years have seen the rapid development of fairness-aware machine learning in mitigating unfairness or discrimination in decision-making in a wide range of applications. However, much less attention has been paid to the fairness-aware multi-objective optimization, which is indeed commonly seen in real life, such as fair resource allocation problems and data-driven multi-objective optimization problems. This paper aims to illuminate and broaden our understanding of multi-objective optimization from the perspective of fairness. To this end, we start with a discussion of user preferences in multi-objective optimization. Subsequently, we explore its relationship to fairness in machine learning and multi-objective optimization. Following the above discussions, representative cases of fairness-aware multi-objective optimization are presented, further elaborating the importance of fairness in traditional multi-objective optimization, data-driven optimization and federated optimization. Finally, challenges and opportunities in fairness-aware multi-objective optimization are addressed. We hope that this article makes a solid step forward towards understanding fairness in the context of optimization. Additionally, we aim to promote research interests in fairness-aware multi-objective optimization.

近年来，公平感知机器学习在减轻各种应用中决策中的不公平或歧视方面取得了快速发展。然而，对于现实生活中确实常见的公平感知多目标优化，人们却很少关注，比如公平资源分配问题和数据驱动的多目标优化问题。本文旨在从公平性的角度阐明和拓宽我们对多目标优化的理解。为此，我们首先讨论多目标优化中的用户偏好。随后，我们探讨了它与机器学习和多目标优化中公平性的关系。在上述讨论之后，提出了公平性感知多目标优化的代表性案例，进一步阐述了公平性在传统多目标优化、数据驱动优化和联邦优化中的重要性。最后，解决了公平感知多目标优化中的挑战和机遇。我们希望本文在理解优化背景下的公平性方面迈出了坚实的一步。此外，我们的目标是促进对公平感知多目标优化的研究兴趣。