BiasLab: A Multilingual, Dual-Framing Framework for Robust Measurement of Output-Level Bias in Large Language Models

2026-01-11

arXiv.org

AI Summary

This work addresses critical limitations in current large language model (LLM) bias evaluation methods—namely, their sensitivity to prompt wording, lack of multilingual support, and poor cross-model comparability—by introducing BiasLab, an open-source, model-agnostic evaluation framework. BiasLab quantifies explicit biases through multilingual symmetric dual-frame probes, randomized instruction wrapping, and a fixed Likert-style response format. The framework innovatively incorporates mirrored probe design, an LLM-judge-driven polarity alignment mechanism, and response normalization strategies, enabling reproducible, cross-lingual, and cross-model bias measurement. It further employs statistical metrics such as effect size and neutrality rate to enhance robustness. Supporting multidimensional bias assessment across demographic, cultural, and political axes, BiasLab automatically generates structured reports and visualizations, offering a reliable benchmark for responsible model deployment.

Technology Category

• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Machine Learning: Large Multimodal Models (LMMs)
• Philosophy and Ethics of AI: Bias, Fairness & Equity

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Large Language Models (LLMs) are increasingly deployed in high-stakes contexts where their outputs influence real-world decisions. However, evaluating bias in LLM outputs remains methodologically challenging due to sensitivity to prompt wording, limited multilingual coverage, and the lack of standardized metrics that enable reliable comparison across models. This paper introduces BiasLab, an open-source, model-agnostic evaluation framework for quantifying output-level (extrinsic) bias through a multilingual, robustness-oriented experimental design. BiasLab constructs mirrored probe pairs under a strict dual-framing scheme: an affirmative assertion favoring Target A and a reverse assertion obtained by deterministic target substitution favoring Target B, while preserving identical linguistic structure. To reduce dependence on prompt templates, BiasLab performs repeated evaluation under randomized instructional wrappers and enforces a fixed-choice Likert response format to maximize comparability across models and languages. Responses are normalized into agreement labels using an LLM-based judge, aligned for polarity consistency across framings, and aggregated into quantitative bias indicators with descriptive statistics including effect sizes and neutrality rates. The framework supports evaluation across diverse bias axes, including demographic, cultural, political, and geopolitical topics, and produces reproducible artifacts such as structured reports and comparative visualizations. BiasLab contributes a standardized methodology for cross-lingual and framing-sensitive bias measurement that complements intrinsic and dataset-based audits, enabling researchers and institutions to benchmark robustness and make better-informed deployment decisions.

Problem

• bias measurement
• large language models
• multilingual evaluation
• output-level bias
• standardized metrics

Innovation

• dual-framing
• multilingual bias evaluation
• output-level bias
• robustness-oriented design
• model-agnostic framework