# Fair Overlap Number of Balls (Fair-ONB): A Data-Morphology-based Undersampling Method for Bias Reduction

arXiv: https://arxiv.org/abs/2407.14210 · HTML: https://mlsanity.com/paper/2407.14210/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 37 references that are in the map.
- First posted on arXiv 19 Jul 2024.

## Builds on (most cited first, 14 of 14)

- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Inherent Trade-Offs in the Fair Determination of Risk Scores](https://mlsanity.com/paper/1609.05807/) (2016)
- [Fairness in Machine Learning: A Survey](https://mlsanity.com/paper/2010.04053/) (2020)
- [Algorithmic Fairness Datasets: the Story so Far](https://mlsanity.com/paper/2202.01711/) (2022)
- [Data-Centric Artificial Intelligence](https://mlsanity.com/paper/2212.11854/) (2022)
- [Fair Active Learning](https://mlsanity.com/paper/2001.01796/) (2020)
- [Multi-FR: A Multi-objective Optimization Framework for Multi-stakeholder Fairness-aware Recommendation](https://mlsanity.com/paper/2105.02951/) (2021)
- [Automatic Fairness Testing of Neural Classifiers through Adversarial Sampling](https://mlsanity.com/paper/2107.08176/) (2021)
- [The Impact of Data Preparation on the Fairness of Software Systems](https://mlsanity.com/paper/1910.02321/) (2019)
- [Revisiting Data Complexity Metrics Based on Morphology for Overlap and Imbalance: Snapshot, New Overlap Number of Balls Metrics and Singular Problems Prospect](https://mlsanity.com/paper/2007.07935/) (2020)
- [Classification of Imbalanced Data with a Geometric Digraph Family](https://mlsanity.com/paper/1904.04564/) (2019)
- [FairBalance: How to Achieve Equalized Odds With Data Pre-processing](https://mlsanity.com/paper/2107.08310/) (2021)
- [Fair Oversampling Technique using Heterogeneous Clusters](https://mlsanity.com/paper/2305.13875/) (2023)
- [Overlap Number of Balls Model-Agnostic CounterFactuals (ONB-MACF): A Data-Morphology-based Counterfactual Generation Method for Trustworthy Artificial Intelligence](https://mlsanity.com/paper/2405.12326/) (2024)

## Abstract (arXiv)

One of the key issues regarding classification problems in Trustworthy Artificial Intelligence is ensuring Fairness in the prediction of different classes when protected (sensitive) features are present. Data quality is critical in these cases, as biases in training data can be reflected in machine learning, impacting human lives and failing to comply with current regulations. One strategy to improve data quality and avoid these problems is preprocessing the dataset. Instance selection via undersampling can foster balanced learning of classes and protected feature values. Performing undersampling in class overlap areas close to the decision boundary should bolster the impact on the classifier. This work proposes Fair Overlap Number of Balls (Fair-ONB), an undersampling method that harnesses the data morphology of the different data groups (obtained from the combination of classes and protected feature values) to perform guided undersampling in overlap areas. It employs attributes of the ball coverage of the groups, such as the radius, number of covered instances and density, to select the most suitable areas for undersampling and reduce bias. Results show that the Fair-ONB method improves model Fairness with low impact on the classifier's predictive performance.