# On Fairness, Diversity and Randomness in Algorithmic Decision Making

arXiv: https://arxiv.org/abs/1706.10208 · HTML: https://mlsanity.com/paper/1706.10208/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jun 2017.

## Built on by (most cited first, 17 of 17)

- [DP-SGD vs PATE: Which Has Less Disparate Impact on Model Accuracy?](https://mlsanity.com/paper/2106.12576/) (2021)
- [Fairness Transferability Subject to Bounded Distribution Shift](https://mlsanity.com/paper/2206.00129/) (2022)
- [Towards Understanding Fairness and its Composition in Ensemble Machine Learning](https://mlsanity.com/paper/2212.04593/) (2022)
- [The Dataset Multiplicity Problem: How Unreliable Data Impacts Predictions](https://mlsanity.com/paper/2304.10655/) (2023)
- [FAIR-Ensemble: When Fairness Naturally Emerges From Deep Ensembling](https://mlsanity.com/paper/2303.00586/) (2023)
- [Mitigating Unfairness via Evolutionary Multi-objective Ensemble Learning](https://mlsanity.com/paper/2210.16754/) (2022)
- [An Empirical Study of Modular Bias Mitigators and Ensembles](https://mlsanity.com/paper/2202.00751/) (2022)
- [Adapting Fairness Interventions to Missing Values](https://mlsanity.com/paper/2305.19429/) (2023)
- [FEAMOE: Fair, Explainable and Adaptive Mixture of Experts](https://mlsanity.com/paper/2210.04995/) (2022)
- [Improving Fairness with Ensemble Combination: Margin-Dependent Bounds](https://mlsanity.com/paper/2301.10813/) (2023)
- [Metric-Fair Classifier Derandomization](https://mlsanity.com/paper/2206.07826/) (2022)
- [Counterfactually Comparing Abstaining Classifiers](https://mlsanity.com/paper/2305.10564/) (2023)
- [Navigating Ensemble Configurations for Algorithmic Fairness](https://mlsanity.com/paper/2210.05594/) (2022)
- [Software Engineering Principles for Fairer Systems: Experiments with GroupCART](https://mlsanity.com/paper/2504.12587/) (2025)
- [OxEnsemble: Fair Ensembles for Low-Data Classification](https://mlsanity.com/paper/2512.09665/) (2025)
- [Can Ensembling Pre-processing Algorithms Lead to Better Machine Learning Fairness?](https://mlsanity.com/paper/2212.02614/) (2022)
- [Randomized Transport Plans via Hierarchical Fully Probabilistic Design](https://mlsanity.com/paper/2408.02701/) (2024)

## Abstract (arXiv)

Consider a binary decision making process where a single machine learning classifier replaces a multitude of humans. We raise questions about the resulting loss of diversity in the decision making process. We study the potential benefits of using random classifier ensembles instead of a single classifier in the context of fairness-aware learning and demonstrate various attractive properties: (i) an ensemble of fair classifiers is guaranteed to be fair, for several different measures of fairness, (ii) an ensemble of unfair classifiers can still achieve fair outcomes, and (iii) an ensemble of classifiers can achieve better accuracy-fairness trade-offs than a single classifier. Finally, we introduce notions of distributional fairness to characterize further potential benefits of random classifier ensembles.