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

AI systems lack stable value systems across cultures: The study found "fundamental instability in value systems" when testing 20+ leading AI models (including GPT-4o, Claude, and Qwen) on ethical dilemmas, meaning these systems don't consistently apply the same values even when facing similar situations—a critical concern for high-stakes decisions like healthcare or criminal justice.

Both Western and Chinese AI models have significant blind spots: Western models show persistent U.S.-centric biases while China-origin models, despite emphasizing multilingual integration, still fail at robust cross-cultural generalization. This means neither region's approach alone provides a solution for truly global AI governance.

Younger people are systematically underrepresented in AI decision-making: The research identified "systematic under-representation of younger demographics" in how these models make decisions, suggesting AI systems may not adequately consider the perspectives and needs of younger populations—an equity issue that could affect policy and services affecting youth.

Bigger AI models aren't necessarily better at understanding different cultures: The study found "non-linear relationships between model scale and alignment quality," meaning that simply making AI systems larger doesn't automatically make them better at respecting diverse cultural values—suggesting the approach to training matters more than raw size.

How AI systems are trained matters more than their underlying architecture: Full-parameter fine-tuning on diverse datasets outperformed other methods, indicating that responsible AI governance requires intentional, inclusive training practices rather than relying on architectural choices alone.