The Fairness Fair: Bringing Human Perception into Collective Decision-Making
This work addresses the gap between theoretical fairness models and human perception in real-world problems, which is incremental as it builds on existing literature to highlight challenges.
The paper argues that existing fairness studies in collective decision-making fail to capture human perception intricacies, proposing that fair solutions should be governed by human cognition and societal factors, and identifies open research directions.
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.