Humboldt-Universität zu Berlin - Kultur-, Sozial- und Bildungswissenschaftliche Fakultät - Institut für Asien- und Afrikawissenschaften

Emerging economies face significant challenges in managing safety risks from powerful technological systems. Indeed, many analysts have identified China as the most likely source of a major accident linked to emerging technologies. Yet, contrary to these expectations, China has achieved a remarkable safety record in certain technological domains, such as civil aviation and nuclear power. How? We theorize that, for industries in which one firm’s accident damages the reputation of all others, international industry associations can contribute to improved safety standards in emerging economies. When firms share a collective reputation, industry associations exert positive peer pressure by subsidizing laggards’ efforts to raise their safety standards and protecting members from public naming and shaming. This departs from existing theories of international private regulation on certification clubs that set strict quality, safety, and environmental standards to deny association benefits to non-members. To demonstrate differences between these two mechanisms, we examine interactions between international industry associations and Chinese firms in three high-risk technological domains: nuclear power, civil aviation, and chemicals. Our findings have implications for scholars interested in the interdependencies between international public regulation and private regulation as well as policymakers trying to manage the safety risks of emerging technologies such as artificial intelligence.

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Bio:

Jeffrey Ding is an Assistant Professor of Political Science at George Washington University. Previously, he was a postdoctoral fellow at Stanford's Center for International Security and Cooperation, sponsored by Stanford's Institute for Human-Centered Artificial Intelligence. He researches great power competition and cooperation in emerging technologies, the political economy of innovation, and China's scientific and technological capabilities. His book, Technology and the Rise of Great Powers (Princeton University Press, 2024), investigates how past technological revolutions influenced the rise and fall of great powers, with implications for U.S.-China competition in emerging technologies like AI. Other work has been published or is forthcoming in European Journal of International Relations, European Journal of International Security, Foreign Affairs, International Studies Quarterly, Review of International Political Economy, and Security Studies, and his research has been cited in The Washington Post, The Financial Times, and other outlets.

There has been a flurry of recent concern about the question of who directly controls large language models.  We show through six studies that coordinated propaganda from powerful global political institutions already indirectly influences the output of U.S. large language models (LLMs) via their training data, a pattern which is easiest to see in China. First, we demonstrate that material originating from China's Publicity Department appears in large quantities in open-source pre-training datasets. Second, we connect this to U.S.-based commercial LLMs by showing that they have memorized sequences of propaganda, suggesting that it does appear in their training data. Third, we use an open-weight LLM to show that additional pre-training on Chinese state propaganda generates more positive answers to prompts about Chinese political institutions and leaders---evidence that propaganda itself, not mere differences in culture and language, can be a causal factor in the behavioral differences we observe across languages.  Fourth, we show that prompting commercial models in Chinese generates more positive responses about China's institutions and leaders than the same queries in English.  Fifth, we show that this language difference holds in prompts of actual Chinese-speaking users. Sixth, we extend our findings with a cross-national study that indicates that the languages of countries with lower media freedom show a stronger pro-regime valence than those with higher media freedom. Finally, we show results that demonstrate that the phenomenon described here is broader than propaganda and state media alone. Our findings join the ample recent work demonstrating the persuasive power of LLMs. Together, these results suggest the troubling conclusion that states and powerful institutions will have increased strategic incentives to disseminate propaganda in the hopes of poisoning LLM training data.

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Bio:

Eddie Yang is an Assistant Professor of Political Science and faculty member in the Institute for Physical Artificial Intelligence at Purdue University. He received his Ph.D. in political science from the University of California San Diego. Yang studies the politics of innovation and technology. His research has been published at the Proceedings of the National Academy of Sciences, and Political Analysis, among other outlets.