Cross-cultural value alignment frameworks for responsible AI governance: Evidence from China-West comparative analysis

TL;DR

This study develops and validates a Multi-Layered Auditing Platform for Responsible AI to systematically evaluate cross-cultural value alignment in China-origin and Western-origin LLMs. By integrating the Ethical Dilemma Corpus, the Diversity-Enhanced Framework, First-Token Probability Alignment, and the MARK framework, the authors diagnose stability, fidelity, and interpretability of moral reasoning across cultures, revealing universal challenges (value instability, demographic under-representation) and regional divergences (multilingual data emphasis vs architectural experimentation). The results show that neither regional paradigm achieves robust cross-cultural generalization, with Mistral-series models outperforming Llama-3-series in cross-cultural alignment and FFT generally preserving cultural variation better than RLHF. The work argues for hybrid alignment strategies, explicit value constraints, and governance frameworks that support transparent auditing, demographically aware bias mitigation, and human-in-the-loop safety protocols to guide responsible AI deployment globally.

Abstract

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...

Figures (19)

• Figure 1: Key research gaps in cross-cultural value alignment for LLMs include: instability in moral reasoning (e.g., sensitivity to prompts and consequences), cultural biases (e.g., U.S.-centric or English-dominant preferences), under-representation of diverse demographics (e.g., younger groups or non-Western values), lack of temporal stability in ethical decisions, and insufficient interpretability in alignment methods.
• Figure 2: Multi-Layered Auditing Platform for Responsible AI, integrating four methodologies: Ethical Dilemma Corpus (for temporal stability), Diversity-Enhanced Framework (DEF, for cultural fidelity), First-Token Probability Alignment (for distributional accuracy), and Multi-stAge Reasoning frameworK (MARK, for interpretable decision-making).
• Figure 3: Pipeline for Ethical Dilemma Corpus: This component assesses temporal stability in LLM moral choices through path-dependent ethical dilemmas.
• Figure 4: Pipeline for Diversity-Enhanced Framework (DEF): DEF generates diverse simulations to quantify cultural fidelity against benchmarks like the World Values Survey. The pipeline focuses on overcoming repetitive outputs. Adapted from Liu25Towards.
• Figure 5: Pipeline for Multi-stAge Reasoning frameworK (MARK): MARK enhances interpretability by simulating personality-driven reasoning based on MBTI theory. Adapted from liu2025demographics.