# Minimax Group Fairness: Algorithms and Experiments

arXiv: https://arxiv.org/abs/2011.03108 · HTML: https://mlsanity.com/paper/2011.03108/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Nov 2020.

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

- [Towards Unbiased and Accurate Deferral to Multiple Experts](https://mlsanity.com/paper/2102.13004/) (2021)
- [Understanding and Improving Fairness-Accuracy Trade-offs in Multi-Task Learning](https://mlsanity.com/paper/2106.02705/) (2021)
- [Rethinking Bias Mitigation: Fairer Architectures Make for Fairer Face Recognition](https://mlsanity.com/paper/2210.09943/) (2022)
- [Minimax Demographic Group Fairness in Federated Learning](https://mlsanity.com/paper/2201.08304/) (2022)
- [Technical Challenges for Training Fair Neural Networks](https://mlsanity.com/paper/2102.06764/) (2021)
- [Robustness Disparities in Commercial Face Detection](https://mlsanity.com/paper/2108.12508/) (2021)
- [Federating for Learning Group Fair Models](https://mlsanity.com/paper/2110.01999/) (2021)
- [Comparing Human and Machine Bias in Face Recognition](https://mlsanity.com/paper/2110.08396/) (2021)
- [Robustness Disparities in Face Detection](https://mlsanity.com/paper/2211.15937/) (2022)
- [Lexicographically Fair Learning: Algorithms and Generalization](https://mlsanity.com/paper/2102.08454/) (2021)
- [On Biases in a UK Biobank-based Retinal Image Classification Model](https://mlsanity.com/paper/2408.02676/) (2024)
- [Distributionally Robust Data Join](https://mlsanity.com/paper/2202.05797/) (2022)
- [On the Vulnerability of Fairness Constrained Learning to Malicious Noise](https://mlsanity.com/paper/2307.11892/) (2023)
- [Beyond Impossibility: Balancing Sufficiency, Separation and Accuracy](https://mlsanity.com/paper/2205.12327/) (2022)
- [Investigating the Effects of Fairness Interventions Using Pointwise Representational Similarity](https://mlsanity.com/paper/2305.19294/) (2023)
- [Federated Fairness without Access to Sensitive Groups](https://mlsanity.com/paper/2402.14929/) (2024)
- [How Technology Impacts and Compares to Humans in Socially Consequential Arenas](https://mlsanity.com/paper/2211.03554/) (2022)

## Abstract (arXiv)

We consider a recently introduced framework in which fairness is measured by worst-case outcomes across groups, rather than by the more standard differences between group outcomes. In this framework we provide provably convergent oracle-efficient learning algorithms (or equivalently, reductions to non-fair learning) for minimax group fairness. Here the goal is that of minimizing the maximum loss across all groups, rather than equalizing group losses. Our algorithms apply to both regression and classification settings and support both overall error and false positive or false negative rates as the fairness measure of interest. They also support relaxations of the fairness constraints, thus permitting study of the tradeoff between overall accuracy and minimax fairness. We compare the experimental behavior and performance of our algorithms across a variety of fairness-sensitive data sets and show empirical cases in which minimax fairness is strictly and strongly preferable to equal outcome notions.