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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.03533 |
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| _version_ | 1866914445406502912 |
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| author | Semitsu, Takayuki Kiribuchi, Naoto Zenitani, Kengo |
| author_facet | Semitsu, Takayuki Kiribuchi, Naoto Zenitani, Kengo |
| contents | 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 outcomes, and that some document pairs yield high disagreements across models. A human evaluation by three experts on two document pairs shows high inter-annotator agreement, while model scores still differ from human judgments. These findings support comparative inspection of policy documents. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_03533 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Automated Analysis of Global AI Safety Initiatives: A Taxonomy-Driven LLM Approach Semitsu, Takayuki Kiribuchi, Naoto Zenitani, Kengo Artificial Intelligence 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 outcomes, and that some document pairs yield high disagreements across models. A human evaluation by three experts on two document pairs shows high inter-annotator agreement, while model scores still differ from human judgments. These findings support comparative inspection of policy documents. |
| title | Automated Analysis of Global AI Safety Initiatives: A Taxonomy-Driven LLM Approach |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2604.03533 |