The anticipated positive social impact of regulatory processes requires both the accuracy and efficiency of their application. Modern artificial intelligence technologies, including natural language processing and machine-assisted reasoning, hold great promise for addressing this challenge. We present a framework to address the challenge of tools for regulatory application, based on current state-of-the-art (SOTA) methods for natural language processing (large language models or LLMs) and formalization of legal reasoning (the legal representation system PROLEG). As an example, we focus on Article 6 of the European General Data Protection Regulation (GDPR). In our framework, a single LLM prompt simultaneously transforms legal text into if-then rules and a corresponding PROLEG encoding, which are then validated and refined by legal domain experts. The final output is an executable PROLEG program that can produce human-readable explanations for instances of GDPR decisions. We describe processes to support the end-to-end transformation of a segment of a regulatory document (Article 6 from GDPR), including the prompting frame to guide an LLM to "compile" natural language text to if-then rules, then to further "compile" the vetted if-then rules to PROLEG. Finally, we produce an instance that shows the PROLEG execution. We conclude by summarizing the value of this approach and note observed limitations with suggestions to further develop such technologies for capturing and deploying regulatory frameworks.

翻译：监管流程预期产生的积极社会影响，既要求其应用的准确性，也要求其效率。包括自然语言处理和机器辅助推理在内的现代人工智能技术，在应对这一挑战方面展现出巨大潜力。我们提出了一个框架，以应对监管应用工具的挑战，该框架基于当前最先进的自然语言处理方法（大语言模型或LLM）和法律推理形式化（法律表示系统PROLEG）。我们以《欧洲通用数据保护条例》（GDPR）第6条为例进行说明。在我们的框架中，单一的LLM提示同时将法律文本转换为if-then规则及相应的PROLEG编码，随后由法律领域专家进行验证和精炼。最终输出是一个可执行的PROLEG程序，能够为GDPR决策实例生成人类可读的解释。我们描述了支持监管文档片段（GDPR第6条）端到端转换的流程，包括引导LLM将自然语言文本“编译”为if-then规则的提示框架，以及进一步将经过审查的if-then规则“编译”为PROLEG的过程。最后，我们展示了一个PROLEG执行的实例。我们总结了该方法的价值，并指出了观察到的局限性，同时提出了进一步发展此类技术以捕获和部署监管框架的建议。