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

arXiv: https://arxiv.org/abs/2601.11702 · HTML: https://mlsanity.com/paper/2601.11702/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 21 references that are in the map.
- First posted on arXiv 16 Jan 2026.

## Builds on (most cited first, 13 of 13)

- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [Interactive Model Cards: A Human-Centered Approach to Model Documentation](https://mlsanity.com/paper/2205.02894/) (2022)
- [Towards AI Accountability Infrastructure: Gaps and Opportunities in AI Audit Tooling](https://mlsanity.com/paper/2402.17861/) (2024)
- [Responsible AI Pattern Catalogue: A Collection of Best Practices for AI Governance and Engineering](https://mlsanity.com/paper/2209.04963/) (2022)
- [The Sanction of Authority: Promoting Public Trust in AI](https://mlsanity.com/paper/2102.04221/) (2021)
- [Use case cards: a use case reporting framework inspired by the European AI Act](https://mlsanity.com/paper/2306.13701/) (2023)
- [The Situate AI Guidebook: Co-Designing a Toolkit to Support Multi-Stakeholder Early-stage Deliberations Around Public Sector AI Proposals](https://mlsanity.com/paper/2402.18774/) (2024)
- [The Pace of Artificial Intelligence Innovations: Speed, Talent, and Trial-and-Error](https://mlsanity.com/paper/2009.01812/) (2020)
- [Auditing of AI: Legal, Ethical and Technical Approaches](https://mlsanity.com/paper/2407.06235/) (2024)
- [Artificial intelligence adoption in the physical sciences, natural sciences, life sciences, social sciences and the arts and humanities: A bibliometric analysis of research publications from 1960-2021](https://mlsanity.com/paper/2306.09145/) (2023)
- [Policy Maps: Tools for Guiding the Unbounded Space of LLM Behaviors](https://mlsanity.com/paper/2409.18203/) (2024)
- [Impact Assessment Card: Communicating Risks and Benefits of AI Uses](https://mlsanity.com/paper/2508.18919/) (2025)

## Built on by (most cited first, 1 of 1)

- [UGAF-ITS: A Standards Harmonization Framework and Validation Tool for Multi-Framework AI Governance in Distributed Intelligent Transportation Systems](https://mlsanity.com/paper/2604.22789/) (2026)

## Abstract (arXiv)

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 (ρ ≥ .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.