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 languag
Record details
Published: 4 April 2026
Source: arXiv
Category: Research
Topics: Regulation · Safety & alignment
Retrieved: 14 July 2026
Related evidence
These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.
Structural Rigidity and the 57-Token Predictive Window: A Physical Framework for Inference-Layer Governability in Large Language Models
arXiv · 4 April 2026
Cognitive Comparability and the Limits of Governance: Evaluating Authority Under Radical Capability Asymmetry
arXiv · 3 April 2026
APPA: Adaptive Preference Pluralistic Alignment for Fair Federated RLHF of LLMs
arXiv · 5 April 2026
How Alignment Routes: Localizing, Scaling, and Controlling Policy Circuits in Language Models
arXiv · 6 April 2026
Incompleteness of AI Safety Verification via Kolmogorov Complexity
arXiv · 6 April 2026
Physiological and Semantic Patterns in Medical Teams Using an Intelligent Tutoring System
arXiv · 31 March 2026
How to cite this record
ethics.ai (4 April 2026), “Automated Analysis of Global AI Safety Initiatives: A Taxonomy-Driven LLM Approach,” evidence record 6371, https://ethics.ai/record/6371 (originally published by arXiv).
Use and limitations
This page is a stable index and citation surface for a source record. ethics.ai did not author the underlying report and has not independently verified every claim. Automatic topics may be imperfect. For consequential use, quote and cite the original publisher.