Fairness is one of the most desirable societal principles in collective decision-making. It has been extensively studied in the past decades for its axiomatic properties and has received substantial attention from the multiagent systems community in recent years for its theoretical and computational aspects in algorithmic decision-making. However, these studies are often not sufficiently rich to capture the intricacies of human perception of fairness in the ambivalent nature of the real-world problems. We argue that not only fair solutions should be deemed desirable by social planners (designers), but they should be governed by human and societal cognition, consider perceived outcomes based on human judgement, and be verifiable. We discuss how achieving this goal requires a broad transdisciplinary approach ranging from computing and AI to behavioral economics and human-AI interaction. In doing so, we identify shortcomings and long-term challenges of the current literature of fair division, describe recent efforts in addressing them, and more importantly, highlight a series of open research directions.

翻译：公平性是集体决策中最令人向往的社会原则之一。过去几十年，学界对其公理性质进行了广泛研究，近年来多智能体系统领域也因其在算法决策中的理论与计算层面给予了极大关注。然而，这些研究往往不足以充分捕捉现实问题矛盾本质中人类对公平性感知的复杂性。我们认为，不仅公平解决方案应被社会规划者（设计者）视为理想目标，更应受人类与社会认知的引导，考虑基于人类判断的感知结果，并具备可验证性。我们探讨了实现这一目标需要从计算与人工智能到行为经济学以及人机交互的广泛跨学科方法。在此过程中，我们指出了当前公平分配文献的不足与长期挑战，描述了近期应对这些挑战的努力，更重要的是，强调了一系列开放的研究方向。