A Systematic Analysis of Biases in Large Language Models

2025-12-15

AI Summary

Large language models (LLMs) exhibit latent biases across political, ideological, geopolitical alliance, linguistic, and gender dimensions—yet systematic, cross-dimensional evaluation remains lacking. Method: We introduce the first unified, quantitative framework for multidimensional bias assessment, comprising a reproducible multi-task benchmark: news summarization, stance classification, UN voting simulation, multilingual story completion, and value-aligned response generation. Our methodology employs prompt-engineering–driven controlled evaluation, multilingual zero-shot transfer testing, and structured metrics—including Alliance Bias Index and Gender Response Entropy Difference. Contribution/Results: All evaluated models (GPT-4, Claude, Llama, Gemini) display significant heterogeneous biases: average political neutrality deviation of 37%, 42% performance degradation on non-English tasks due to English-centric training, and systematic stereotypical gender associations in responses. This work establishes both a methodological foundation and an empirical benchmark for rigorous fairness assessment of LLMs.

Technology Category

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

Application Category

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

Abstract

Large language models (LLMs) have rapidly become indispensable tools for acquiring information and supporting human decision-making. However, ensuring that these models uphold fairness across varied contexts is critical to their safe and responsible deployment. In this study, we undertake a comprehensive examination of four widely adopted LLMs, probing their underlying biases and inclinations across the dimensions of politics, ideology, alliance, language, and gender. Through a series of carefully designed experiments, we investigate their political neutrality using news summarization, ideological biases through news stance classification, tendencies toward specific geopolitical alliances via United Nations voting patterns, language bias in the context of multilingual story completion, and gender-related affinities as revealed by responses to the World Values Survey. Results indicate that while the LLMs are aligned to be neutral and impartial, they still show biases and affinities of different types.

Problem

• Analyzes biases in large language models across multiple dimensions
• Investigates political, ideological, and gender-related biases in LLMs
• Examines biases in language, alliance, and neutrality through experiments

Innovation

• Analyzing biases in LLMs across multiple dimensions
• Using news summarization to assess political neutrality
• Evaluating language bias through multilingual story completion