# What is Fair? Exploring Pareto-Efficiency for Fairness Constrained Classifiers

arXiv: https://arxiv.org/abs/1910.14120 · HTML: https://mlsanity.com/paper/1910.14120/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Oct 2019.

## Built on by (most cited first, 12 of 12)

- [Correct-N-Contrast: A Contrastive Approach for Improving Robustness to Spurious Correlations](https://mlsanity.com/paper/2203.01517/) (2022)
- [Bias Mitigation for Machine Learning Classifiers: A Comprehensive Survey](https://mlsanity.com/paper/2207.07068/) (2022)
- [Understanding and Improving Fairness-Accuracy Trade-offs in Multi-Task Learning](https://mlsanity.com/paper/2106.02705/) (2021)
- [On Learning Fairness and Accuracy on Multiple Subgroups](https://mlsanity.com/paper/2210.10837/) (2022)
- [BARACK: Partially Supervised Group Robustness With Guarantees](https://mlsanity.com/paper/2201.00072/) (2021)
- [Fairness Without Harm: An Influence-Guided Active Sampling Approach](https://mlsanity.com/paper/2402.12789/) (2024)
- [Pareto Efficient Fairness in Supervised Learning: From Extraction to Tracing](https://mlsanity.com/paper/2104.01634/) (2021)
- [Intersectional Unfairness Discovery](https://mlsanity.com/paper/2405.20790/) (2024)
- [Finding Pareto Trade-offs in Fair and Accurate Detection of Toxic Speech](https://mlsanity.com/paper/2204.07661/) (2022)
- [A Hybrid 2-stage Neural Optimization for Pareto Front Extraction](https://mlsanity.com/paper/2101.11684/) (2021)
- [Scalable Unidirectional Pareto Optimality for Multi-Task Learning with Constraints](https://mlsanity.com/paper/2110.15442/) (2021)
- [Rank-Constrained Adaptation for Reliable Real-World Performance](https://mlsanity.com/paper/2602.06924/) (2026)

## Abstract (arXiv)

The potential for learned models to amplify existing societal biases has been broadly recognized. Fairness-aware classifier constraints, which apply equality metrics of performance across subgroups defined on sensitive attributes such as race and gender, seek to rectify inequity but can yield non-uniform degradation in performance for skewed datasets. In certain domains, imbalanced degradation of performance can yield another form of unintentional bias. In the spirit of constructing fairness-aware algorithms as societal imperative, we explore an alternative: Pareto-Efficient Fairness (PEF). Theoretically, we prove that PEF identifies the operating point on the Pareto curve of subgroup performances closest to the fairness hyperplane, maximizing multiple subgroup accuracy. Empirically we demonstrate that PEF outperforms by achieving Pareto levels in accuracy for all subgroups compared to strict fairness constraints in several UCI datasets.