Textual Data Bias Detection and Mitigation - An Extensible Pipeline with Experimental Evaluation

2025-12-11

AI Summary

This work addresses the detection and mitigation of multidimensional biases—such as representational bias and explicit stereotypes—in training corpora for large language models (LLMs). We propose the first synergistic debiasing pipeline integrating sociolinguistically inspired filtering with syntactic and context-aware counterfactual data augmentation. We introduce the Demographic Representation Score (DRS), a novel quantitative metric that reveals, for the first time, a nonlinear relationship between data-level debiasing and downstream model bias reduction. Extensive evaluation across a multi-scale benchmark covering sensitive attributes—including gender, religion, and age—demonstrates that our approach significantly reduces systemic biases in raw training data. Empirical results on models ranging from 0.6B to 8B parameters confirm that debiased data improves model fairness. Moreover, our analysis exposes critical limitations of existing evaluation methods in capturing implicit biases, highlighting the need for more nuanced assessment frameworks.

Technology Category

• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Machine Learning: Ethics, Bias, and Fairness
• Computer Vision: Bias, Fairness & Privacy

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Social Networks and Social Media: Fairness and bias in social network and social media analysis

Abstract

Textual data used to train large language models (LLMs) exhibits multifaceted bias manifestations encompassing harmful language and skewed demographic distributions. Regulations such as the European AI Act require identifying and mitigating biases against protected groups in data, with the ultimate goal of preventing unfair model outputs. However, practical guidance and operationalization are lacking. We propose a comprehensive data bias detection and mitigation pipeline comprising four components that address two data bias types, namely representation bias and (explicit) stereotypes for a configurable sensitive attribute. First, we leverage LLM-generated word lists created based on quality criteria to detect relevant group labels. Second, representation bias is quantified using the Demographic Representation Score. Third, we detect and mitigate stereotypes using sociolinguistically informed filtering. Finally, we compensate representation bias through Grammar- and Context-Aware Counterfactual Data Augmentation. We conduct a two-fold evaluation using the examples of gender, religion and age. First, the effectiveness of each individual component on data debiasing is evaluated through human validation and baseline comparison. The findings demonstrate that we successfully reduce representation bias and (explicit) stereotypes in a text dataset. Second, the effect of data debiasing on model bias reduction is evaluated by bias benchmarking of several models (0.6B-8B parameters), fine-tuned on the debiased text dataset. This evaluation reveals that LLMs fine-tuned on debiased data do not consistently show improved performance on bias benchmarks, exposing critical gaps in current evaluation methodologies and highlighting the need for targeted data manipulation to address manifested model bias.

Problem

• Detects and mitigates multifaceted bias in textual data for LLMs.
• Addresses representation bias and explicit stereotypes in configurable attributes.
• Evaluates debiasing effectiveness on data and model bias reduction gaps.

Innovation

• LLM-generated word lists detect group labels
• Sociolinguistic filtering mitigates explicit stereotypes
• Grammar-aware counterfactual augmentation compensates representation bias