# Distributive Justice and Fairness Metrics in Automated Decision-making: How Much Overlap Is There?

arXiv: https://arxiv.org/abs/2105.01441 · HTML: https://mlsanity.com/paper/2105.01441/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 45 references that are in the map.
- First posted on arXiv 4 May 2021.

## Builds on (most cited first, 5 of 5)

- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Multiaccuracy: Black-Box Post-Processing for Fairness in Classification](https://mlsanity.com/paper/1805.12317/) (2018)
- [Fairness in Machine Learning: Lessons from Political Philosophy](https://mlsanity.com/paper/1712.03586/) (2017)
- [On Formalizing Fairness in Prediction with Machine Learning](https://mlsanity.com/paper/1710.03184/) (2017)
- [A Moral Framework for Understanding of Fair ML through Economic Models of Equality of Opportunity](https://mlsanity.com/paper/1809.03400/) (2018)

## Built on by (most cited first, 10 of 10)

- [Enforcing Group Fairness in Algorithmic Decision Making: Utility Maximization Under Sufficiency](https://mlsanity.com/paper/2206.02237/) (2022)
- [Fairness in Algorithmic Profiling: A German Case Study](https://mlsanity.com/paper/2108.04134/) (2021)
- [What Is Fairness? On the Role of Protected Attributes and Fictitious Worlds](https://mlsanity.com/paper/2205.09622/) (2022)
- [Uncertainty-aware predictive modeling for fair data-driven decisions](https://mlsanity.com/paper/2211.02730/) (2022)
- [Navigating Fairness Measures and Trade-Offs](https://mlsanity.com/paper/2307.08484/) (2023)
- [Distributive Justice as the Foundational Premise of Fair ML: Unification, Extension, and Interpretation of Group Fairness Metrics](https://mlsanity.com/paper/2206.02897/) (2022)
- [Insights From Insurance for Fair Machine Learning](https://mlsanity.com/paper/2306.14624/) (2023)
- [What Is the Point of Equality in Machine Learning Fairness? Beyond Equality of Opportunity](https://mlsanity.com/paper/2506.16782/) (2025)
- [Fair Machine Learning in Healthcare: A Review](https://mlsanity.com/paper/2206.14397/) (2022)
- [Voices of Freelance Professional Writers on AI: Limitations, Expectations, and Fears](https://mlsanity.com/paper/2504.05008/) (2025)

## Abstract (arXiv)

The advent of powerful prediction algorithms led to increased automation of high-stake decisions regarding the allocation of scarce resources such as government spending and welfare support. This automation bears the risk of perpetuating unwanted discrimination against vulnerable and historically disadvantaged groups. Research on algorithmic discrimination in computer science and other disciplines developed a plethora of fairness metrics to detect and correct discriminatory algorithms. Drawing on robust sociological and philosophical discourse on distributive justice, we identify the limitations and problematic implications of prominent fairness metrics. We show that metrics implementing equality of opportunity only apply when resource allocations are based on deservingness, but fail when allocations should reflect concerns about egalitarianism, sufficiency, and priority. We argue that by cleanly distinguishing between prediction tasks and decision tasks, research on fair machine learning could take better advantage of the rich literature on distributive justice.