Project Synesis is a multilingual discovery dashboard for foreign AI governance documents. It provides direct access to primary sources from the full range of institutional actors within a jurisdiction's AI policy ecosystem, with nuanced translation notes that remain faithful to source-language context and deep-dive reports to serve users of varying expertise. Explore documents across all AI governance domains with a built-in bilingual glossary, 22 filtering tags, and a full-text archive.
Some foreign AI policy translation doesn't map cleanly across languages, since distinct terms that carry different regulatory weight can collapse into the same English word. Meanwhile, some of the most informative primary sources, such as academic journals and ministry guidance, rarely surface through English-dominant browsers. Project Synesis aggregates these primary sources in one place, preserves original-language context, and pairs them with in-depth reports produced by our research team. We currently focus on China and plan to expand to additional jurisdictions.
The dashboard surfaces relevant primary sources for anyone researching AI policy and risk in a specific region. No source-language fluency or prior familiarity with foreign policy ecosystems is required, though less familiar users may find the reports a more useful entry point.
The database covers the full spectrum of AI policy activity across every jurisdiction tracked, including national regulations, ministry guidance, military doctrine, think tank analysis, and more. Every entry anchors to the original-language primary source, and cites secondary sources where they add meaningful context. A structured tagging system classifies entries across 22 policy tags in three categories: governance domains, international dimensions, and risk framing. Read the full methodology →
Yilin is a student at Amherst College studying Math and Political Science. She is a MATS 10.0 Scholar and affiliated with UChicago XLab. Her work focuses on US-China AI coordination, compute governance, and AI R&D automation risk thresholds.
Yixiong is a student at Georgia Tech. He leads the Georgia Tech AI Safety Initiative, co-founded Second Look Research, and is affiliated with CAIS, Gray Swan AI, and UChicago XLab.
Yi Mei is a Master’s student at Georgia Tech with full-time experience building technical systems. Her work across high-stakes industries has driven a deep interest in the responsible development of AI.
Linh is an Amherst College undergraduate studying Political Science and Philosophy. Her work is centered on AI geopolitics, with a specialized focus on middle-power AI strategies and compute governance.
We are grateful to Parv Mahajan, whose early ideation informed the foundation of this project. We thank Edward Kembery, Emmie Hine, Gabriel Wagner, and Ben Hayum for their feedback on source coverage and analytical framing.