# Properties of fairness measures in the context of varying class imbalance and protected group ratios

arXiv: https://arxiv.org/abs/2411.08425 · HTML: https://mlsanity.com/paper/2411.08425/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 52 references that are in the map.
- First posted on arXiv 13 Nov 2024.

## Builds on (most cited first, 20 of 20)

- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016)
- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Fair prediction with disparate impact: A study of bias in recidivism prediction instruments](https://mlsanity.com/paper/1610.07524/) (2016)
- [Retiring Adult: New Datasets for Fair Machine Learning](https://mlsanity.com/paper/2108.04884/) (2021)
- [Algorithmic decision making and the cost of fairness](https://mlsanity.com/paper/1701.08230/) (2017)
- [Fairness in Machine Learning: A Survey](https://mlsanity.com/paper/2010.04053/) (2020)
- [Fairness in Criminal Justice Risk Assessments: The State of the Art](https://mlsanity.com/paper/1703.09207/) (2017)
- [Empirical Risk Minimization under Fairness Constraints](https://mlsanity.com/paper/1802.08626/) (2018)
- [A comparative study of fairness-enhancing interventions in machine learning](https://mlsanity.com/paper/1802.04422/) (2018)
- [A Clarification of the Nuances in the Fairness Metrics Landscape](https://mlsanity.com/paper/2106.00467/) (2021)
- [On Formalizing Fairness in Prediction with Machine Learning](https://mlsanity.com/paper/1710.03184/) (2017)
- [Trustworthy AI](https://mlsanity.com/paper/2011.02272/) (2020)
- [Algorithmic Fairness Datasets: the Story so Far](https://mlsanity.com/paper/2202.01711/) (2022)
- [dalex: Responsible Machine Learning with Interactive Explainability and Fairness in Python](https://mlsanity.com/paper/2012.14406/) (2020)
- [General Fair Empirical Risk Minimization](https://mlsanity.com/paper/1901.10080/) (2019)
- [FIFA: Making Fairness More Generalizable in Classifiers Trained on Imbalanced Data](https://mlsanity.com/paper/2206.02792/) (2022)
- [Towards A Holistic View of Bias in Machine Learning: Bridging Algorithmic Fairness and Imbalanced Learning](https://mlsanity.com/paper/2207.06084/) (2022)
- [Addressing Fairness, Bias and Class Imbalance in Machine Learning: the FBI-loss](https://mlsanity.com/paper/2105.06345/) (2021)
- [Bias, Fairness, and Accountability with AI and ML Algorithms](https://mlsanity.com/paper/2105.06558/) (2021)
- [Fairness and Explainability in Automatic Decision-Making Systems. A challenge for computer science and law](https://mlsanity.com/paper/2206.03226/) (2022)

## Built on by (most cited first, 7 of 7)

- [Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond](https://mlsanity.com/paper/2507.08866/) (2025)
- [Bias Begins with Data: The FairGround Corpus for Robust and Reproducible Research on Algorithmic Fairness](https://mlsanity.com/paper/2510.22363/) (2025)
- [Uncovering Fairness through Data Complexity as an Early Indicator](https://mlsanity.com/paper/2504.05923/) (2025)
- [Reducing Class Bias In Data-Balanced Datasets Through Hardness-Based Resampling](https://mlsanity.com/paper/2504.07031/) (2025)
- [Simple and Effective Specialized Representations for Fair Classifiers](https://mlsanity.com/paper/2505.11740/) (2025)
- [No evaluation without fair representation : Impact of label and selection bias on the evaluation, performance and mitigation of classification models](https://mlsanity.com/paper/2603.09662/) (2026)
- [A Rank Graduation metric for Algorithmic fairness](https://mlsanity.com/paper/2609.39025/) (2026)

## Abstract (arXiv)

Society is increasingly relying on predictive models in fields like criminal justice, credit risk management, or hiring. To prevent such automated systems from discriminating against people belonging to certain groups, fairness measures have become a crucial component in socially relevant applications of machine learning. However, existing fairness measures have been designed to assess the bias between predictions for protected groups without considering the imbalance in the classes of the target variable. Current research on the potential effect of class imbalance on fairness focuses on practical applications rather than dataset-independent measure properties. In this paper, we study the general properties of fairness measures for changing class and protected group proportions. For this purpose, we analyze the probability mass functions of six of the most popular group fairness measures. We also measure how the probability of achieving perfect fairness changes for varying class imbalance ratios. Moreover, we relate the dataset-independent properties of fairness measures described in this paper to classifier fairness in real-life tasks. Our results show that measures such as Equal Opportunity and Positive Predictive Parity are more sensitive to changes in class imbalance than Accuracy Equality. These findings can help guide researchers and practitioners in choosing the most appropriate fairness measures for their classification problems.