针对多功能AI的复杂风险，提出以管理机制为主的灵活监管新范式。
Regulating Multifunctionality
- 用管理导向的监管替代一刀切规则，鼓励开发者主动管控风险。
- 传统性能标准和事后追责难奏效，因监控与执行成本过高。
- 适合政策制定者、AI治理研究者及高阶技术管理者阅读参考。
基础模型与生成式人工智能加剧了人工智能固有的异质性挑战。由于其本质特性，这些模型能为用户执行多种功能，从而带来广泛多样的风险。这种多功能性意味着预设的、统一的监管方式不可行。即使通常具有灵活性的性能标准和事后责任机制，在监控与执行困难的背景下，也难以有效应对多功能AI的风险。因此，监管者应转而推动开发方和使用者主动进行风险管理，采用在其他异质性场景中已被证明有效的管理型监管模式。同时，监管机构需保持持续警觉与敏捷响应能力。相较于其他领域，多功能AI的监管更需要充足资源、顶尖人才与领导力，以及致力于监管卓越的组织文化。
原文摘要 · Abstract (English)
Foundation models and generative artificial intelligence (AI) exacerbate a core regulatory challenge associated with AI: its heterogeneity. By their very nature, foundation models and generative AI can perform multiple functions for their users, thus presenting a vast array of different risks. This multifunctionality means that prescriptive, one-size-fits-all regulation will not be a viable option. Even performance standards and ex post liability - regulatory approaches that usually afford flexibility - are unlikely to be strong candidates for responding to multifunctional AI's risks, given challenges in monitoring and enforcement. Regulators will do well instead to promote proactive risk management on the part of developers and users by using management-based regulation, an approach that has proven effective in other contexts of heterogeneity. Regulators will also need to maintain ongoing vigilance and agility. More than in other contexts, regulators of multifunctional AI will need sufficient resources, top human talent and leadership, and organizational cultures committed to regulatory excellence.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。