Bridging AI and Regulation: Large Language Models for Documentation Compliance Check

Abstract

As Artificial Intelligence (AI) permeates our lives more rapidly, robust regulations to ensure trustworthy AI applications are demanded by AI operators and consumers. The European Union’s AI Act addresses this by establishing a regulatory framework for high-risk AI systems, emphasizing the need for proper documentation to ensure compliance. This paper presents a novel approach to assess AI documentation using Large Language Model based methods: a GPT-4 prompting approach and fine-tuning DeBERTa and Mistral-7B. Due to the lack of relevant datasets, we constructed a novel benchmark dataset comprising text passages from AI research publications. These passages are matched by AI experts with regulatory requirements and are classified into different fulfilment classes. Using this dataset in our comparative study, our findings demonstrate that fine-tuning the DeBERTa model achieves 92% ± 1% accuracy in classifying compliance categories, outperforming the more complex GPT-4 and Mistral-7B significantly. Overall, this research advances AI governance by providing insights into automating documentation compliance checks. Finally, by making the models and datasets publicly available, we promote further research into enhancing transparency and accountability in AI systems.

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