# Equal Confusion Fairness: Measuring Group-Based Disparities in Automated Decision Systems

arXiv: https://arxiv.org/abs/2307.00472 · HTML: https://mlsanity.com/paper/2307.00472/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 55 references that are in the map.
- First posted on arXiv 2 Jul 2023.

## Builds on (most cited first, 11 of 11)

- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness](https://mlsanity.com/paper/1711.05144/) (2017)
- [Persistent Anti-Muslim Bias in Large Language Models](https://mlsanity.com/paper/2101.05783/) (2021)
- [Fairness in Criminal Justice Risk Assessments: The State of the Art](https://mlsanity.com/paper/1703.09207/) (2017)
- [Mitigating Bias in Algorithmic Hiring: Evaluating Claims and Practices](https://mlsanity.com/paper/1906.09208/) (2019)
- [An Intersectional Definition of Fairness](https://mlsanity.com/paper/1807.08362/) (2018)
- [Assessing Gender Bias in Machine Translation -- A Case Study with Google Translate](https://mlsanity.com/paper/1809.02208/) (2018)
- [A Clarification of the Nuances in the Fairness Metrics Landscape](https://mlsanity.com/paper/2106.00467/) (2021)
- [Group Fairness: Independence Revisited](https://mlsanity.com/paper/2101.02968/) (2021)
- [Fairness in the Eyes of the Data: Certifying Machine-Learning Models](https://mlsanity.com/paper/2009.01534/) (2020)
- [System Cards for AI-Based Decision-Making for Public Policy](https://mlsanity.com/paper/2203.04754/) (2022)

## Built on by (most cited first, 3 of 3)

- [Facets of Disparate Impact: Evaluating Legally Consistent Bias in Machine Learning](https://mlsanity.com/paper/2505.05471/) (2025)
- [Sy-FAR: Symmetry-based Fair Adversarial Robustness](https://mlsanity.com/paper/2509.12939/) (2025)
- [Man and machine: artificial intelligence and judicial decision making](https://mlsanity.com/paper/2603.19042/) (2026)

## Abstract (arXiv)

As artificial intelligence plays an increasingly substantial role in decisions affecting humans and society, the accountability of automated decision systems has been receiving increasing attention from researchers and practitioners. Fairness, which is concerned with eliminating unjust treatment and discrimination against individuals or sensitive groups, is a critical aspect of accountability. Yet, for evaluating fairness, there is a plethora of fairness metrics in the literature that employ different perspectives and assumptions that are often incompatible. This work focuses on group fairness. Most group fairness metrics desire a parity between selected statistics computed from confusion matrices belonging to different sensitive groups. Generalizing this intuition, this paper proposes a new equal confusion fairness test to check an automated decision system for fairness and a new confusion parity error to quantify the extent of any unfairness. To further analyze the source of potential unfairness, an appropriate post hoc analysis methodology is also presented. The usefulness of the test, metric, and post hoc analysis is demonstrated via a case study on the controversial case of COMPAS, an automated decision system employed in the US to assist judges with assessing recidivism risks. Overall, the methods and metrics provided here may assess automated decision systems' fairness as part of a more extensive accountability assessment, such as those based on the system accountability benchmark.