Cross-cultural value alignment frameworks for responsible AI governance: Evidence from China-West comparative analysis
This addresses the critical governance challenge of cross-cultural value alignment for LLMs affecting global AI deployment, though it appears incremental as it builds on existing auditing frameworks with comparative analysis.
This study tackled the problem of ensuring Large Language Models align with diverse cultural values by developing a Multi-Layered Auditing Platform to evaluate China-origin and Western-origin LLMs, revealing universal challenges like fundamental instability in value systems and systematic under-representation of younger demographics, with findings showing Mistral-series architectures significantly outperform LLaMA3-series in cross-cultural alignment.
As Large Language Models (LLMs) increasingly influence high-stakes decision-making across global contexts, ensuring their alignment with diverse cultural values has become a critical governance challenge. This study presents a Multi-Layered Auditing Platform for Responsible AI that systematically evaluates cross-cultural value alignment in China-origin and Western-origin LLMs through four integrated methodologies: Ethical Dilemma Corpus for assessing temporal stability, Diversity-Enhanced Framework (DEF) for quantifying cultural fidelity, First-Token Probability Alignment for distributional accuracy, and Multi-stAge Reasoning frameworK (MARK) for interpretable decision-making. Our comparative analysis of 20+ leading models, such as Qwen, GPT-4o, Claude, LLaMA, and DeepSeek, reveals universal challenges-fundamental instability in value systems, systematic under-representation of younger demographics, and non-linear relationships between model scale and alignment quality-alongside divergent regional development trajectories. While China-origin models increasingly emphasize multilingual data integration for context-specific optimization, Western models demonstrate greater architectural experimentation but persistent U.S.-centric biases. Neither paradigm achieves robust cross-cultural generalization. We establish that Mistral-series architectures significantly outperform LLaMA3-series in cross-cultural alignment, and that Full-Parameter Fine-Tuning on diverse datasets surpasses Reinforcement Learning from Human Feedback in preserving cultural variation...