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LLM-Assisted Content Conditional Debiasing for Fair Text Embedding

Wenlong Deng, Blair Chen, Beidi Zhao, Chiyu Zhang, Xiaoxiao Li, Christos Thrampoulidis

TL;DR

This paper defines a novel content-conditional equal distance (CCED) fairness for text embeddings, ensuring content-conditional independence between sensitive attributes and text embeddings and introduces a content-conditional debiasing (CCD) loss to ensure that embeddings of texts with different sensitive attributes but identical content maintain the same distance from the embedding of their corresponding neutral text.

Abstract

Mitigating biases in machine learning models has become an increasing concern in Natural Language Processing (NLP), particularly in developing fair text embeddings, which are crucial yet challenging for real-world applications like search engines. In response, this paper proposes a novel method for learning fair text embeddings. First, we define a novel content-conditional equal distance (CCED) fairness for text embeddings, ensuring content-conditional independence between sensitive attributes and text embeddings. Building on CCED, we introduce a content-conditional debiasing (CCD) loss to ensure that embeddings of texts with different sensitive attributes but identical content maintain the same distance from the embedding of their corresponding neutral text. Additionally, we tackle the issue of insufficient training data by using Large Language Models (LLMs) with instructions to fairly augment texts into different sensitive groups. Our extensive evaluations show that our approach effectively enhances fairness while maintaining the utility of embeddings. Furthermore, our augmented dataset, combined with the CCED metric, serves as an new benchmark for evaluating fairness.

LLM-Assisted Content Conditional Debiasing for Fair Text Embedding

TL;DR

This paper defines a novel content-conditional equal distance (CCED) fairness for text embeddings, ensuring content-conditional independence between sensitive attributes and text embeddings and introduces a content-conditional debiasing (CCD) loss to ensure that embeddings of texts with different sensitive attributes but identical content maintain the same distance from the embedding of their corresponding neutral text.

Abstract

Mitigating biases in machine learning models has become an increasing concern in Natural Language Processing (NLP), particularly in developing fair text embeddings, which are crucial yet challenging for real-world applications like search engines. In response, this paper proposes a novel method for learning fair text embeddings. First, we define a novel content-conditional equal distance (CCED) fairness for text embeddings, ensuring content-conditional independence between sensitive attributes and text embeddings. Building on CCED, we introduce a content-conditional debiasing (CCD) loss to ensure that embeddings of texts with different sensitive attributes but identical content maintain the same distance from the embedding of their corresponding neutral text. Additionally, we tackle the issue of insufficient training data by using Large Language Models (LLMs) with instructions to fairly augment texts into different sensitive groups. Our extensive evaluations show that our approach effectively enhances fairness while maintaining the utility of embeddings. Furthermore, our augmented dataset, combined with the CCED metric, serves as an new benchmark for evaluating fairness.
Paper Structure (20 sections, 1 theorem, 13 equations, 2 figures, 10 tables, 1 algorithm)

This paper contains 20 sections, 1 theorem, 13 equations, 2 figures, 10 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Method
  4. Problem Setting
  5. Content Conditional Debiasing
  6. Fairness Definition
  7. Content Conditional Debiasing Loss
  8. LLM-Assisted Content Conditional Data Augmentation
  9. Experiments
  10. Augmentation Quality Checking
  11. Results and Analysis
  12. Conclusion
  13. Limitaions
  14. Ethical Consideration
  15. Algorithm Details
  16. ...and 5 more sections

Key Result

Theorem 3.3

When the equal probability assumption holds, achieving content conditioned equal distance fairness is equivalent to achieving conditional independence between sensitive attributes and content $A \perp C' ~|~ C$.

Figures (2)

  • Figure 1: Pipleline of our method with gender as the sensitive attributes. (a) Graphical demonstration of the fairness issue. (b) The debiasing procedure achieves a content-conditioned equal distance to improve the fairness. (c) Overview of the data augmentation strategy, including the prompt template used to replace sensitive words with their equivalents from all sensitive groups. (d) Prompt search module: Augmented texts are sent to the demographic polarity checking block. Incorrectly augmented samples are then manually labeled and added to the prompts.
  • Figure 2: T-SNE plots of embeddings that are processed by different methods. Our approach maintains embedding positions similar to BERT while mixing male and female embeddings thus achieving fairness.

Theorems & Definitions (3)

  • Definition 3.1
  • Theorem 3.3
  • proof