# Harm Ratio: A Novel and Versatile Fairness Criterion

arXiv: https://arxiv.org/abs/2410.02977 · HTML: https://mlsanity.com/paper/2410.02977/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 58 references that are in the map.
- First posted on arXiv 3 Oct 2024.

## Builds on (most cited first, 18 of 18)

- [Algorithmic Fairness](https://mlsanity.com/paper/2001.09784/) (2020)
- [Proportionally Fair Clustering](https://mlsanity.com/paper/1905.03674/) (2019)
- [Proportional Participatory Budgeting with Additive Utilities](https://mlsanity.com/paper/2008.13276/) (2020)
- [PeerReview4All: Fair and Accurate Reviewer Assignment in Peer Review](https://mlsanity.com/paper/1806.06237/) (2018)
- [Bridging Machine Learning and Mechanism Design towards Algorithmic Fairness](https://mlsanity.com/paper/2010.05434/) (2020)
- [Participatory Budgeting: Models and Approaches](https://mlsanity.com/paper/2003.00606/) (2020)
- [Fairness in Federated Learning via Core-Stability](https://mlsanity.com/paper/2211.02091/) (2022)
- [Fair Allocation of Indivisible Public Goods](https://mlsanity.com/paper/1805.03164/) (2018)
- [Fair Public Decision Making](https://mlsanity.com/paper/1611.04034/) (2016)
- [Best of Both Worlds: Ex-Ante and Ex-Post Fairness in Resource Allocation](https://mlsanity.com/paper/2005.14122/) (2020)
- [The Core of the Participatory Budgeting Problem](https://mlsanity.com/paper/1610.03474/) (2016)
- [Phragm'en's Voting Methods and Justified Representation](https://mlsanity.com/paper/2102.12305/) (2021)
- [Fair Division of Indivisible Goods: A Survey](https://mlsanity.com/paper/2202.07551/) (2022)
- [Pabulib: A Participatory Budgeting Library](https://mlsanity.com/paper/2012.06539/) (2020)
- [Fairness Concepts for Indivisible Items with Externalities](https://mlsanity.com/paper/2110.09066/) (2021)
- [On the Existence of Pareto Efficient and Envy Free Allocations](https://mlsanity.com/paper/1906.07257/) (2019)
- [Beyond Cake Cutting: Allocating Homogeneous Divisible Goods](https://mlsanity.com/paper/2201.04662/) (2022)
- [Cardinal-Utility Matching Markets: The Quest for Envy-Freeness, Pareto-Optimality, and Efficient Computability](https://mlsanity.com/paper/2402.08851/) (2024)

## Built on by (most cited first, 1 of 1)

- [Altruism and Fair Objective in Mixed-Motive Markov games](https://mlsanity.com/paper/2602.08389/) (2026)

## Abstract (arXiv)

Envy-freeness has become the cornerstone of fair division research. In settings where each individual is allocated a disjoint share of collective resources, it is a compelling fairness axiom which demands that no individual strictly prefer the allocation of another individual to their own. Unfortunately, in many real-life collective decision-making problems, the goal is to choose a (common) public outcome that is equally applicable to all individuals, and the notion of envy becomes vacuous. Consequently, this literature has avoided studying fairness criteria that focus on individuals feeling a sense of jealousy or resentment towards other individuals (rather than towards the system), missing out on a key aspect of fairness. In this work, we propose a novel fairness criterion, individual harm ratio, which is inspired by envy-freeness but applies to a broad range of collective decision-making settings. Theoretically, we identify minimal conditions under which this criterion and its groupwise extensions can be guaranteed, and study the computational complexity of related problems. Empirically, we conduct experiments with real data to show that our fairness criterion is powerful enough to differentiate between prominent decision-making algorithms for a range of tasks from voting and fair division to participatory budgeting and peer review.