  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.
