# Toward a better trade-off between performance and fairness with kernel-based distribution matching

arXiv: https://arxiv.org/abs/1910.11779 · HTML: https://mlsanity.com/paper/1910.11779/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Oct 2019.

## Built on by (most cited first, 30 of 36)

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- [Understanding and Improving Fairness-Accuracy Trade-offs in Multi-Task Learning](https://mlsanity.com/paper/2106.02705/) (2021)
- [A Stochastic Optimization Framework for Fair Risk Minimization](https://mlsanity.com/paper/2102.12586/) (2021)
- [Mitigating Racial Biases in Toxic Language Detection with an Equity-Based Ensemble Framework](https://mlsanity.com/paper/2109.13137/) (2021)
- [Fast and Efficient MMD-based Fair PCA via Optimization over Stiefel Manifold](https://mlsanity.com/paper/2109.11196/) (2021)
- [Stochastic Differentially Private and Fair Learning](https://mlsanity.com/paper/2210.08781/) (2022)
- [FRAPPE: A Group Fairness Framework for Post-Processing Everything](https://mlsanity.com/paper/2312.02592/) (2023)
- [InfoFair: Information-Theoretic Intersectional Fairness](https://mlsanity.com/paper/2105.11069/) (2021)
- [A Survey on Fairness-aware Recommender Systems](https://mlsanity.com/paper/2306.00403/) (2023)
- [Fairness Without Harm: An Influence-Guided Active Sampling Approach](https://mlsanity.com/paper/2402.12789/) (2024)
- [How Robust is Your Fairness? Evaluating and Sustaining Fairness under Unseen Distribution Shifts](https://mlsanity.com/paper/2207.01168/) (2022)
- [Fairness and robustness in anti-causal prediction](https://mlsanity.com/paper/2209.09423/) (2022)
- [The Role of Learning Algorithms in Collective Action](https://mlsanity.com/paper/2405.06582/) (2024)
- [Malign Overfitting: Interpolation Can Provably Preclude Invariance](https://mlsanity.com/paper/2211.15724/) (2022)
- [Deceptive Fairness Attacks on Graphs via Meta Learning](https://mlsanity.com/paper/2310.15653/) (2023)
- [Simpson's Paradox in Recommender Fairness: Reconciling differences between per-user and aggregated evaluations](https://mlsanity.com/paper/2210.07755/) (2022)
- [On the Inductive Biases of Demographic Parity-based Fair Learning Algorithms](https://mlsanity.com/paper/2402.18129/) (2024)
- [Fairness via In-Processing in the Over-parameterized Regime: A Cautionary Tale](https://mlsanity.com/paper/2206.14853/) (2022)
- [f-FERM: A Scalable Framework for Robust Fair Empirical Risk Minimization](https://mlsanity.com/paper/2312.03259/) (2023)
- [Hyper-parameter Tuning for Fair Classification without Sensitive Attribute Access](https://mlsanity.com/paper/2302.01385/) (2023)
- [Enhancing Group Fairness in Online Settings Using Oblique Decision Forests](https://mlsanity.com/paper/2310.11401/) (2023)
- [Inducing Group Fairness in Prompt-Based Language Model Decisions](https://mlsanity.com/paper/2406.16738/) (2024)
- [Do You See What I See? Capabilities and Limits of Automated Multimedia Content Analysis](https://mlsanity.com/paper/2201.11105/) (2021)
- [FITNESS: A Causal De-correlation Approach for Mitigating Bias in Machine Learning Software](https://mlsanity.com/paper/2305.14396/) (2023)
- [Towards Fairness and Privacy: A Novel Data Pre-processing Optimization Framework for Non-binary Protected Attributes](https://mlsanity.com/paper/2410.00836/) (2024)
- [Adaptation and Generalization for Unknown Sensitive Factors of Variations](https://mlsanity.com/paper/2107.13625/) (2021)
- [Classification Utility, Fairness, and Compactness via Tunable Information Bottleneck and R'enyi Measures](https://mlsanity.com/paper/2206.10043/) (2022)
- [Towards A Scalable Solution for Improving Multi-Group Fairness in Compositional Classification](https://mlsanity.com/paper/2307.05728/) (2023)

## Abstract (arXiv)

As recent literature has demonstrated how classifiers often carry unintended biases toward some subgroups, deploying machine learned models to users demands careful consideration of the social consequences. How should we address this problem in a real-world system? How should we balance core performance and fairness metrics? In this paper, we introduce a MinDiff framework for regularizing classifiers toward different fairness metrics and analyze a technique with kernel-based statistical dependency tests. We run a thorough study on an academic dataset to compare the Pareto frontier achieved by different regularization approaches, and apply our kernel-based method to two large-scale industrial systems demonstrating real-world improvements.