# Fairness as Equality of Opportunity: Normative Guidance from Political Philosophy

arXiv: https://arxiv.org/abs/2106.08259 · HTML: https://mlsanity.com/paper/2106.08259/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 23 references that are in the map.
- First posted on arXiv 15 Jun 2021.

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

- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016)
- [Fair prediction with disparate impact: A study of bias in recidivism prediction instruments](https://mlsanity.com/paper/1610.07524/) (2016)
- [Inherent Trade-Offs in the Fair Determination of Risk Scores](https://mlsanity.com/paper/1609.05807/) (2016)
- [Measurement and Fairness](https://mlsanity.com/paper/1912.05511/) (2019)
- [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, 4 of 4)

- [The Possibility of Fairness: Revisiting the Impossibility Theorem in Practice](https://mlsanity.com/paper/2302.06347/) (2023)
- [Counterfactuals for the Future](https://mlsanity.com/paper/2212.03974/) (2022)
- [The Fairness Field Guide: Perspectives from Social and Formal Sciences](https://mlsanity.com/paper/2201.05216/) (2022)
- [What Is the Point of Equality in Machine Learning Fairness? Beyond Equality of Opportunity](https://mlsanity.com/paper/2506.16782/) (2025)

## Abstract (arXiv)

Recent interest in codifying fairness in Automated Decision Systems (ADS) has resulted in a wide range of formulations of what it means for an algorithmic system to be fair. Most of these propositions are inspired by, but inadequately grounded in, political philosophy scholarship. This paper aims to correct that deficit. We introduce a taxonomy of fairness ideals using doctrines of Equality of Opportunity (EOP) from political philosophy, clarifying their conceptions in philosophy and the proposed codification in fair machine learning. We arrange these fairness ideals onto an EOP spectrum, which serves as a useful frame to guide the design of a fair ADS in a given context. We use our fairness-as-EOP framework to re-interpret the impossibility results from a philosophical perspective, as the in-compatibility between different value systems, and demonstrate the utility of the framework with several real-world and hypothetical examples. Through our EOP-framework we hope to answer what it means for an ADS to be fair from a moral and political philosophy standpoint, and to pave the way for similar scholarship from ethics and legal experts.