AI 中文总结
本文提出利用知识表示、AI、NLP和密码学等计算方法构建监管计算基础设施，以缓解监管复杂性对经济增长的阻碍，并强调将监管视为可计算改进的制度信息系统。
AI 中文摘要
监管复杂性日益被视为创新、机构响应能力和长期经济增长的障碍，据估计，监管积累每年使美国GDP增长率降低近整整一个百分点。本文认为，计算方法——包括知识表示、人工智能、自然语言处理和密码学——可以通过提高效率、透明度和机构响应能力，帮助减少各种形式的监管“繁文缛节”。我们开发了一个框架，用于理解监管摩擦的来源以及如何缓解这些摩擦以支持合规、分析和改革。我们进一步认为，有效的现代化要求不仅将监管视为法律文本，而且将其视为一个复杂的制度和信息系统，该系统可以在保留法律合法性和程序问责制的同时，部分地被表示、分析、协调和计算改进。
英文摘要
Regulatory complexity is increasingly recognized as an impediment to innovation, institutional responsiveness, and long-run economic growth, with regulatory accumulation estimated to reduce the U.S. GDP growth rate by nearly a full percentage point annually. This paper argues that computational approaches--including knowledge representation, artificial intelligence, natural-language processing, and cryptography--can help reduce forms of regulatory "red tape" by improving efficiency, transparency, and institutional responsiveness. We develop a framework for understanding the sources of regulatory friction and how they can be mitigated to support compliance, analysis, and reform. We further argue that effective modernization requires treating regulation not merely as legal text, but as a complex institutional and informational system that can be partially represented, analyzed, coordinated, and improved computationally while preserving legal legitimacy and procedural accountability.
CommentsA report from the CRA-Industry Committee
