Individual Fairness Revisited: Transferring Techniques from Adversarial Robustness

A Tale of Fairness Revisited: Beyond Adversarial Learning for Deep Neural Network Fairness

Motivated by the need for fair algorithmic decision making in the age of automation and artificially-intelligent technology, this technical report provides a theoretical insight into adversarial training for fairness in deep learning. We build upon previous work in adversarial fairness, show the persistent tradeoff between fair predictions and model performance, and explore further mechanisms that help in offsetting this tradeoff.

Transferring Fairness under Distribution Shifts via Fair Consistency Regularization

The increasing reliance on ML models in high-stakes tasks has raised a major concern on fairness violations. Although there has been a surge of work that improves algorithmic fairness, most of them are under the assumption of an identical training and test distribution. In many real-world applications, however, such an assumption is often violated as previously trained fair models are often deployed in a different environment, and the fairness of such models has been observed to collapse. In this paper, we study how to transfer model fairness under distribution shifts, a widespread issue in pr…

Attainability and Optimality: The Equalized Odds Fairness Revisited

Fairness of machine learning algorithms has been of increasing interest. In order to suppress or eliminate discrimination in prediction, various notions as well as approaches have been proposed to impose fairness. Given a notion of fairness, an essential problem is then whether or not it can always be attained, even if with an unlimited amount of data. This issue is, however, not well addressed yet. In this paper, focusing on the Equalized Odds notion of fairness, we consider the attainability of this criterion and, furthermore, if it is attainable, the optimality of the prediction performance…

利用多任务学习在有限人口统计信息下迁移公平性

Transferring Fairness using Multi-Task Learning with Limited Demographic Information

针对目标任务缺少人口统计标注的问题，本文提出多任务公平性损失，借助相关任务标签迁移公平性，并验证其可跨数据集实现交叉公平性。

ProPublica's COMPAS Data Revisited

I examine the COMPAS recidivism risk score and criminal history data collected by ProPublica in 2016 that fueled intense debate and research in the nascent field of 'algorithmic fairness'. ProPublica's COMPAS data is used in an increasing number of studies to test various definitions of algorithmic fairness. This paper takes a closer look at the actual datasets put together by ProPublica. In particular, the sub-datasets built to study the likelihood of recidivism within two years of a defendant's original COMPAS survey screening date. I take a new yet simple approach to visualize these data, b…

TransFair：将公平性从眼部疾病分类迁移到进展预测

TransFair: Transferring Fairness from Ocular Disease Classification to Progression Prediction

针对纵向数据不足导致眼部疾病进展预测存在群体不公平的问题，本文提出TransFair，通过公平感知EfficientNet与知识蒸馏迁移分类模型的公平表征。