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）相关危害实施更严格治理的迫切呼吁，正促使全球范围内采纳监管学者所称的"管理型"监管路径。美国与欧洲近期的举措，以及国际标准化组织通过的主要自律标准，均共享一个核心的管理型范式。这些管理型举措旨在推动加强对AI工具训练与开发过程的人类监督。因此，为适应这一新兴的管理型监管范式，需要对人类引导的训练技术进行完善与系统化。若得到切实执行，人类引导的训练能够缓解AI面临的部分技术与伦理压力，通过人类直觉提升AI性能，并更好地满足公平性与有效可解释性的需求。本文探讨了新兴的AI管理型监管框架与训练过程中人类监督必要性之间的关联。我们广泛概述了人类引导训练涉及的部分技术要素，进而论证监管机构最为关注的高风险AI应用场景，应更依赖于人类引导训练而非纯数据驱动训练。我们希望促进法学学者与计算机科学家之间的对话，共同探讨如何治理这一应用广泛、类型多样且风险动态变化的技术领域。