Pairwise Comparison for Bias Identification and Quantification

2025-12-16

AI Summary

Identifying linguistic bias in online news and social media faces challenges including high subjectivity, strong contextual dependence, and scarcity of high-quality annotations. To address these, we propose a lightweight pairwise-comparison-based framework for bias identification and quantification. Our approach introduces a novel cost-aware pairwise optimization mechanism that jointly fine-tunes matching strategies and scoring parameters. We further construct a controllable simulation environment incorporating latent-variable modeling, noise injection, and annotator bias simulation. Leveraging severity latent distribution modeling, distance-calibrated noise, LLM-based comparative evaluation, and end-to-end simulation-empirical validation, our method significantly improves robustness and annotation efficiency in bias quantification. Empirical evaluation on real-world benchmarks demonstrates superior performance over direct LLM assessment and baseline pairwise methods. The framework establishes a reproducible, scalable paradigm for large-scale subjective language annotation.

Technology Category

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

Application Category

• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Linguistic bias in online news and social media is widespread but difficult to measure. Yet, its identification and quantification remain difficult due to subjectivity, context dependence, and the scarcity of high-quality gold-label datasets. We aim to reduce annotation effort by leveraging pairwise comparison for bias annotation. To overcome the costliness of the approach, we evaluate more efficient implementations of pairwise comparison-based rating. We achieve this by investigating the effects of various rating techniques and the parameters of three cost-aware alternatives in a simulation environment. Since the approach can in principle be applied to both human and large language model annotation, our work provides a basis for creating high-quality benchmark datasets and for quantifying biases and other subjective linguistic aspects. The controlled simulations include latent severity distributions, distance-calibrated noise, and synthetic annotator bias to probe robustness and cost-quality trade-offs. In applying the approach to human-labeled bias benchmark datasets, we then evaluate the most promising setups and compare them to direct assessment by large language models and unmodified pairwise comparison labels as baselines. Our findings support the use of pairwise comparison as a practical foundation for quantifying subjective linguistic aspects, enabling reproducible bias analysis. We contribute an optimization of comparison and matchmaking components, an end-to-end evaluation including simulation and real-data application, and an implementation blueprint for cost-aware large-scale annotation

Problem

• Develop efficient pairwise comparison methods for bias annotation
• Overcome subjectivity and cost in quantifying linguistic bias
• Create high-quality benchmark datasets for reproducible bias analysis

Innovation

• Pairwise comparison reduces annotation effort for bias
• Simulation tests cost-aware alternatives for efficient implementation
• Optimized approach enables reproducible bias quantification in datasets