Wasserstein Fair Classification

2019-07-28 · Ray Jiang, Aldo Pacchiano, Tom Stepleton, Heinrich Jiang, Silvia Chiappa

We propose an approach to fair classification that enforces independence between the classifier outputs and sensitive information by minimizing Wasserstein-1 distances. The approach has desirable theoretical properties and is robust to specific choices of the threshold used to obtain class predictions from model outputs. We introduce different methods that enable hiding sensitive information at test time or have a simple and fast implementation. We show empirical performance against different fairness baselines on several benchmark fairness datasets.

📄 PDF Abstract BibTeX arXiv:1907.12059

Code (1)

deepmind/wasserstein_fairness 공식 구현

Tasks

Classification Fairness General Classification

Fair Text Classification with Wasserstein Independence

2023-11-21 · Thibaud Leteno, Antoine Gourru, Charlotte Laclau, Rémi Emonet 외

Group fairness is a central research topic in text classification, where reaching fair treatment between sensitive groups (e.g. women vs. men) remains an open challenge. This paper presents a novel method for mitigating …

Attribute Classification Fairness text-classification+1

WassFFed: Wasserstein Fair Federated Learning

2024-11-11 · Zhongxuan Han, Li Zhang, Chaochao Chen, Xiaolin Zheng 외

Federated Learning (FL) employs a training approach to address scenarios where users' data cannot be shared across clients. Achieving fairness in FL is imperative since training data in FL is inherently geographically di…

Fairness Federated Learning

Wasserstein Robust Classification with Fairness Constraints

2021-03-11 · Yijie Wang, Viet Anh Nguyen, Grani A. Hanasusanto

We propose a distributionally robust classification model with a fairness constraint that encourages the classifier to be fair in view of the equality of opportunity criterion. We use a type-$\infty$ Wasserstein ambiguit…

Attribute Classification Fairness Robust classification

A Distributionally Robust Approach to Fair Classification

2020-07-18 · Bahar Taskesen, Viet Anh Nguyen, Daniel Kuhn, Jose Blanchet

We propose a distributionally robust logistic regression model with an unfairness penalty that prevents discrimination with respect to sensitive attributes such as gender or ethnicity. This model is equivalent to a tract…

Classification Fairness General Classification regression+1

Wasserstein projection distance for fairness testing of regression models

2025-10-05 · Wanxin Li, Yongjin P. Park, Khanh Dao Duc arxiv

Fairness testing evaluates whether a model satisfies a specified fairness criterion across different groups, yet most research has focused on classification models, leaving regression models underexplored. This paper int…