# The Fairness Fair: Bringing Human Perception into Collective Decision-Making

arXiv: https://arxiv.org/abs/2312.14402 · HTML: https://mlsanity.com/paper/2312.14402/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 61 references that are in the map.
- First posted on arXiv 22 Dec 2023.

## Builds on (most cited first, 12 of 12)

- [Best of Both Worlds: Ex-Ante and Ex-Post Fairness in Resource Allocation](https://mlsanity.com/paper/2005.14122/) (2020)
- [EFX Exists for Three Agents](https://mlsanity.com/paper/2002.05119/) (2020)
- [An Improved Approximation Algorithm for Maximin Shares](https://mlsanity.com/paper/1903.00029/) (2019)
- [Simultaneously Achieving Ex-ante and Ex-post Fairness](https://mlsanity.com/paper/2004.02554/) (2020)
- [Fair Division of Indivisible Goods: A Survey](https://mlsanity.com/paper/2202.07551/) (2022)
- [Fair and Efficient Allocations under Lexicographic Preferences](https://mlsanity.com/paper/2012.07680/) (2020)
- [Social Choice Around the Block: On the Computational Social Choice of Blockchain](https://mlsanity.com/paper/2203.07777/) (2022)
- [Contrastive Fairness in Machine Learning](https://mlsanity.com/paper/1905.07360/) (2019)
- [Fair Division through Information Withholding](https://mlsanity.com/paper/1907.02583/) (2019)
- [Fair in the Eyes of Others](https://mlsanity.com/paper/1911.11053/) (2019)
- [Ordinal Maximin Share Approximation for Chores](https://mlsanity.com/paper/2201.07424/) (2022)
- [Epistemic vs. Counterfactual Fairness in Allocation of Resources](https://mlsanity.com/paper/2212.04574/) (2022)

## Built on by (most cited first, 2 of 2)

- [Distributive Fairness in Large Language Models: Evaluating Alignment with Human Values](https://mlsanity.com/paper/2502.00313/) (2025)
- [Simultaneous Envy and Equitability Guarantees](https://mlsanity.com/paper/2608.26410/) (2026)

## Abstract (arXiv)

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.