Automated Analysis of Global AI Safety Initiatives: A Taxonomy-Driven LLM Approach
We present an automated crosswalk framework that compares an AI safety policy document pair under a shared taxonomy of activities. Using the activity categories defined in Activity Map on AI Safety as fixed aspects, the system extracts and maps relevant activities, then produces for each aspect a short summary for each document, a brief comparison, and a similarity score. We assess the stability and validity of LLM-based crosswalk analysis across public policy documents. Using five large language models, we perform crosswalks on ten publicly available documents and visualize mean similarity scores with a heatmap. The results show that model choice substantially affects the crosswalk outco...
| Authors | Takayuki Semitsu, Naoto Kiribuchi, Kengo Zenitani |
|---|---|
| Primary category | cs.AI |
| Published | 4 April 2026 |
| Last updated at source | 4 April 2026 |
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