Abstract
Group fairness is a central research topic in text classification, where reaching fair treatment between sensitive groups (e.g., women and men) remains an open challenge. We propose an approach that extends the use of the Wasserstein Dependency Measure for learning unbiased neural text classifiers. Given the challenge of distinguishing fair from unfair information in a text encoder, we draw inspiration from adversarial training by inducing independence between representations learned for the target label and those for a sensitive attribute. We further show that Domain Adaptation can be efficiently leveraged to remove the need for access to the sensitive attributes in the dataset we cure. We provide both theoretical and empirical evidence that our approach is well-founded.
Abstract (translated)
群体公平性是文本分类研究中的一个核心课题，其中在敏感群体（例如女性和男性）之间实现公平待遇仍然是一个未解决的挑战。我们提出了一种方法，该方法扩展了Wasserstein依赖度量的应用，以学习无偏见的神经文本分类器。面对区分文本编码器中公正信息与不公正信息的难题，我们借鉴对抗训练的思想，通过诱导目标标签和敏感属性之间表示的独立性来实现这一目的。此外，我们进一步表明，领域适应技术可以有效地利用，从而在处理数据集时无需访问敏感属性。我们提供了理论和实证证据，证明我们的方法是合理且有效的。
URL
https://arxiv.org/abs/2503.07691
