Jason Hung

Jason Hung

This study investigates the shifting global dynamics of Artificial Intelligence (AI) research by analysing the trajectories of countries dominating AI publications between 2000 and 2025. Drawing on the comprehensive OpenAlex datasets and employing fractional counting to avoid double attribution in co-authored work, the research maps the relative shares of AI publications across major global players. The analysis reveals a profound restructuring of the international AI research landscape. The US and the European Union (representing EU27), once the undisputed and established leaders, have experienced a notable decline in relative dominance, with their combined share of publications falling from over 57% in 2000 to less than 25% in 2025. In contrast, China has undergone a dramatic ascent, expanding its global share of AI publications from under 5% in 2000 to nearly 36% by 2025, therefore emerging as the single most dominant contributor. Alongside China, India has also risen substantially, consolidating a multipolar Asian research ecosystem. These empirical findings highlight the strategic implications of concentrated research output, particularly China's capacity to shape the future direction of AI innovation and standard-setting. Beyond publication volume, the study further examines research quality by comparing each country's share of high-impact publications against its overall output, and analyses citation impact trajectories across major players. The findings show that in addition to China leading in volume, the country has also recently led in high-impact publications. Such an observation challenges the general assumption that Western powers retain dominance in high-impact AI scholarship.

Jason Hung

Artificial intelligence (AI) control protocols assume that trusted large language model (LLM) monitors reliably assess proposed actions across all deployment contexts. This paper tests that assumption in the geographic dimension. We audit Claude Opus 4.6-the monitor specified in Apart Research's AI Control Hackathon Track 3 benchmark-for systematic gaps in its factual knowledge of the global AI landscape. We develop the AI Control Knowledge Framework (ACKF), a six-dimension thematic scheme, and operationalise it with 17 verified indicators drawn from the Global AI Dataset v2 (GAID v2): 24,453 indicators across 227 countries published on Harvard Dataverse. A five-category response classification scheme distinguishes verifiable fabrication (VF) from honest refusal (HR); logistic regression with country-clustered standard errors combined with difference-in-differences (DiD) estimation quantifies geographic disparities in monitor accuracy across 2,820 country-metric-year observations. Contrary to our initial hypothesis, Claude Opus 4.6 produces higher fabrication rates for Global North queries than for Global South counterparts-a pattern consistent with a partial-knowledge mechanism in which the model attempts answers more frequently for Global North contexts but commits to incorrect values. This fabrication profile constitutes an exploitable vulnerability, where an adversarial AI system could frame harmful actions in governance or public attitude terms to reduce the probability of detection. This study provides the first cross-national, multi-domain audit of an AI control monitor's geographic knowledge gaps, with direct implications for the design of control protocols.