Fervent calls for more robust governance of the harms associated with artificial intelligence (AI) are leading to the adoption around the world of what regulatory scholars have called a management-based approach to regulation. Recent initiatives in the United States and Europe, as well as the adoption of major self-regulatory standards by the International Organization for Standardization, share in common a core management-based paradigm. These management-based initiatives seek to motivate an increase in human oversight of how AI tools are trained and developed. Refinements and systematization of human-guided training techniques will thus be needed to fit within this emerging era of management-based regulatory paradigm. If taken seriously, human-guided training can alleviate some of the technical and ethical pressures on AI, boosting AI performance with human intuition as well as better addressing the needs for fairness and effective explainability. In this paper, we discuss the connection between the emerging management-based regulatory frameworks governing AI and the need for human oversight during training. We broadly cover some of the technical components involved in human-guided training and then argue that the kinds of high-stakes use cases for AI that appear of most concern to regulators should lean more on human-guided training than on data-only training. We hope to foster a discussion between legal scholars and computer scientists involving how to govern a domain of technology that is vast, heterogenous, and dynamic in its applications and risks.

翻译：对人工智能（AI）相关危害加强治理的强烈呼声，正推动全球范围内采纳被规制学者称为"管理型"的规制方法。美国与欧洲的最新举措，以及国际标准化组织通过的重大自律标准，均共享一个核心的管理型范式。这些管理型举措旨在促进对AI工具训练与开发过程中人类监督的强化。因此，为适应这一新兴的管理型规制范式时代，人类指导训练技术的改进与系统化将成为必需。若得到切实重视，人类指导训练可缓解AI面临的部分技术与伦理压力，通过人类直觉提升AI性能，同时更好满足公平性与有效可解释性的需求。本文探讨了新兴的AI管理型规制框架与训练过程中人类监督需求之间的关联。我们广泛涵盖人类指导训练涉及的部分技术要素，进而提出：监管者最为关切的高风险AI应用场景，应更多依赖人类指导训练而非纯数据训练。我们期望推动法学学者与计算机科学家就如何治理这一应用场景广阔、异质性强且风险动态变化的技术领域展开对话。