[论文解读] Dual Governance: The intersection of centralized regulation and crowdsourced safety mechanisms for Generative AI

Avijit Ghosh, Dhanya Lakshmi|arXiv (Cornell University)|Aug 2, 2023

Law, AI, and Intellectual PropertyComputer Science被引用 3

一句话总结

本文提出双轨治理（Dual Governance）框架，将集中化的美国法规与由社区驱动的开源安全机制相结合，用于生成式人工智能。通过在透明注册表中将监管标准与技术工具相连接，该框架提升了清晰度、一致性、可用性与敏捷性，从而在减轻虚假信息、偏见及知识产权侵权等伦理风险的同时，促进公平创新。

ABSTRACT

Generative Artificial Intelligence (AI) has seen mainstream adoption lately, especially in the form of consumer-facing, open-ended, text and image generating models. However, the use of such systems raises significant ethical and safety concerns, including privacy violations, misinformation and intellectual property theft. The potential for generative AI to displace human creativity and livelihoods has also been under intense scrutiny. To mitigate these risks, there is an urgent need of policies and regulations responsible and ethical development in the field of generative AI. Existing and proposed centralized regulations by governments to rein in AI face criticisms such as not having sufficient clarity or uniformity, lack of interoperability across lines of jurisdictions, restricting innovation, and hindering free market competition. Decentralized protections via crowdsourced safety tools and mechanisms are a potential alternative. However, they have clear deficiencies in terms of lack of adequacy of oversight and difficulty of enforcement of ethical and safety standards, and are thus not enough by themselves as a regulation mechanism. We propose a marriage of these two strategies via a framework we call Dual Governance. This framework proposes a cooperative synergy between centralized government regulations in a U.S. specific context and safety mechanisms developed by the community to protect stakeholders from the harms of generative AI. By implementing the Dual Governance framework, we posit that innovation and creativity can be promoted while ensuring safe and ethical deployment of generative AI.

研究动机与目标

• 解决纯粹集中式AI监管的局限性，后者往往缺乏技术具体性、一致性和敏捷性。
• 克服去中心化、众源安全工具的不足，后者缺乏执行力、监督机制与标准化。
• 创建一种协同治理模式，将监管权威与社区驱动的技术干预相结合，以保障生成式AI的安全。
• 确保大型与小型技术开发者及终端用户都能公平获取安全机制。
• 建立一个动态、可审查的系统，能够适应不断演进的AI技术与监管需求。

提出的方法

• 建立一个集中式注册表，将美国监管要求与特定开源安全工具（如水印技术、模型擦除、数据集过滤）进行映射。
• 将现有开源工具（如glaze用于模型规避防护、erasure用于概念移除、LLM水印技术）整合进标准化的治理框架中。
• 通过定期审查政策与工具，保持框架的相关性，并适应技术与监管的变化。
• 引入举报人机制与人类替代机制，受启发于《人工智能权利法案蓝图》（Blueprint for an AI Bill of Rights），以确保问责性。
• 建立透明、可审计的系统，通过文档化映射确保监管意图与技术实现保持一致。
• 利用现有监管机构（如CFPB）实现可执行的救济途径，并确保对安全标准的合规性。

实验结果

研究问题

• RQ1 如何有意义地整合集中式监管与去中心化安全机制，以改善生成式AI的治理？
• RQ2 一个治理框架需要满足哪些标准，才能确保AI安全的清晰度、一致性、可用性、敏捷性与透明性？
• RQ3 开源安全工具在多大程度上可与政府监管有效对齐，以构建一个协调一致的监管生态系统？
• RQ4 该框架如何在生成式AI技术快速演进的背景下保持相关性与适应性？
• RQ5 哪些机制可确保大型企业与小型实体都能公平地获取并实施安全措施？

主要发现

• 双轨治理框架成功弥补了集中式监管与众源安全机制的关键缺陷，通过将两者优势整合为统一、透明的系统。
• 将法规与特定技术工具进行映射，提升了清晰度，确保监管意图在技术上可实现且可验证。
• 定期审查法规与工具，使框架能够保持敏捷性，及时响应新兴AI风险与技术创新。
• 该框架通过为中小型开发者提供可访问且成本低廉的安全机制，支持了公平创新。
• 通过引入可执行救济与透明机制，框架提升了对生成式AI系统的问责性与用户信任。
• 该模型表明，混合治理模式在一致性、可用性与敏捷性方面，可达到单一模式无法企及的更高水平。