Mitigating biases in machine learning models has gained increasing attention
in Natural Language Processing (NLP). Yet, only a few studies focus on fair
text embeddings, which are crucial yet challenging for real-world applications.
In this paper, we propose a novel method for learning fair text embeddings. We
achieve fairness while maintaining utility trade-off by ensuring conditional
independence between sensitive attributes and text embeddings conditioned on
the content. Specifically, we enforce that embeddings of texts with different
sensitive attributes but identical content maintain the same distance toward
the embedding of their corresponding neutral text. Furthermore, we address the
issue of lacking proper training data by using Large Language Models (LLMs) to
augment texts into different sensitive groups. Our extensive evaluations
demonstrate that our approach effectively improves fairness while preserving
the utility of embeddings, representing a pioneering effort in achieving
conditional independence for fair text embeddings.

在自然语言处理领域，解决机器学习模型中的偏见问题日益受到关注。本文提出了一种学习公平文本嵌入的新方法，通过确保在内容条件下敏感属性与文本嵌入之间的条件独立性，同时实现公平性并保持效用平衡。通过使用大型语言模型将文本扩充为不同敏感组，解决了缺乏适当训练数据的问题。我们的广泛评估表明，我们的方法在提高公平性的同时保持了嵌入的实用性，是在实现公平文本嵌入的条件独立性方面的先驱性工作。