February 21, 2024Open Access

Content Conditional Debiasing for Fair Text Embedding

Key Points

Fair text embeddings ensure that sensitive attributes do not unfairly influence model outputs, improving NLP applications.
Our method maintains utility trade-off, achieving conditional independence across 12 evaluations to boost fairness in text embeddings.
Using large language models, we augment text data to create diverse sensitive groups, enhancing the training process for fairness improvements in embeddings. Our approach sets a precedent for future work on conditional independence in text embedding fairness.

Abstract

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.

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