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arXiv · 2601.11702

PASTA: A Scalable Framework for Multi-Policy AI Compliance Evaluation

Yu Yang·Ig-Jae Kim·Dongwook Yoon
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Abstract

AI compliance is becoming increasingly critical as AI systems grow more powerful and pervasive. Yet the rapid expansion of AI policies creates substantial burdens for resource-constrained practitioners lacking policy expertise. Existing approaches typically address one policy at a time, making multi-policy compliance costly. We present PASTA, a scalable compliance tool integrating four innovations: (1) a comprehensive model-card format supporting descriptive inputs across development stages; (2) a policy normalization scheme; (3) an efficient LLM-powered pairwise evaluation engine with cost-saving strategies; and (4) an interface delivering interpretable evaluations via compliance heatmaps and actionable recommendations. Expert evaluation shows PASTA's judgments closely align with human experts ($\rho \geq .626$). The system evaluates five major policies in under two minutes at approximately \$3. A user study (N = 12) confirms practitioners found outputs easy-to-understand and actionable, introducing a novel framework for scalable automated AI governance.

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Yu Yang, Ig-Jae Kim, Dongwook Yoon. 2026-01-16. PASTA: A Scalable Framework for Multi-Policy AI Compliance Evaluation. https://arxiv.org/abs/2601.11702

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