# Fair Feature Selection: A Comparison of Multi-Objective Genetic Algorithms

arXiv: https://arxiv.org/abs/2310.02752 · HTML: https://mlsanity.com/paper/2310.02752/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 27 references that are in the map.
- First posted on arXiv 4 Oct 2023.

## Builds on (most cited first, 10 of 10)

- [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)
- [Counterfactual Fairness](https://mlsanity.com/paper/1703.06856/) (2017)
- [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)
- [A Survey on the Explainability of Supervised Machine Learning](https://mlsanity.com/paper/2011.07876/) (2020)
- [Fairness in Machine Learning: Lessons from Political Philosophy](https://mlsanity.com/paper/1712.03586/) (2017)
- [How fair can we go in machine learning? Assessing the boundaries of fairness in decision trees](https://mlsanity.com/paper/2006.12399/) (2020)
- [Genetic programming approaches to learning fair classifiers](https://mlsanity.com/paper/2004.13282/) (2020)
- [Fair Feature Subset Selection using Multiobjective Genetic Algorithm](https://mlsanity.com/paper/2205.01512/) (2022)

## Built on by (most cited first, 2 of 2)

- [Evaluating Fair Feature Selection in Machine Learning for Healthcare](https://mlsanity.com/paper/2403.19165/) (2024)
- [Fair Feature Importance Scores via Feature Occlusion and Permutation](https://mlsanity.com/paper/2602.09196/) (2026)

## Abstract (arXiv)

Machine learning classifiers are widely used to make decisions with a major impact on people's lives (e.g. accepting or denying a loan, hiring decisions, etc). In such applications,the learned classifiers need to be both accurate and fair with respect to different groups of people, with different values of variables such as sex and race. This paper focuses on fair feature selection for classification, i.e. methods that select a feature subset aimed at maximising both the accuracy and the fairness of the predictions made by a classifier. More specifically, we compare two recently proposed Genetic Algorithms (GAs) for fair feature selection that are based on two different multi-objective optimisation approaches: (a) a Pareto dominance-based GA; and (b) a lexicographic optimisation-based GA, where maximising accuracy has higher priority than maximising fairness. Both GAs use the same measures of accuracy and fairness, allowing for a controlled comparison. As far as we know, this is the first comparison between the Pareto and lexicographic approaches for fair classification. The results show that, overall, the lexicographic GA outperformed the Pareto GA with respect to accuracy without degradation of the fairness of the learned classifiers. This is an important result because at present nearly all GAs for fair classification are based on the Pareto approach, so these results suggest a promising new direction for research in this area.