认真对待训练：人工智能的人类指导与管理型监管
Taking Training Seriously: Human Guidance and Management-Based Regulation of Artificial Intelligence
AI总结：
本文探讨了AI管理型监管框架与训练中人类监督的联系，指出高风险AI用例应更多依赖人类指导训练而非纯数据训练，以缓解技术伦理压力并提升公平性与可解释性。
摘要：
对与人工智能（AI）相关危害进行更稳健治理的强烈呼吁，正促使世界各地采用监管学者所称的管理型监管方法。美国和欧洲近期的举措，以及国际标准化组织通过的主要自我监管标准，都有一个共同的核心管理型范式。这些基于管理的举措旨在激励增加对AI工具训练和开发方式的人类监督。因此，为了适应这一新兴的管理型监管范式时代，将需要对人类指导的训练技术进行改进和系统化。如果被认真对待，人类指导的训练可以缓解AI面临的一些技术和伦理压力，利用人类直觉提升AI性能，并更好地满足公平性和有效可解释性的需求。在本文中，我们讨论了治理AI的新兴管理型监管框架与训练期间对人类监督需求之间的联系。我们广泛涵盖了人类指导训练中涉及的一些技术组件，然后论证指出，监管机构似乎最关注的高风险AI用例应该更多地依赖人类指导的训练，而不是仅依赖数据的训练。我们希望促进法学者和计算机科学家之间的讨论，探讨如何治理一个在应用和风险方面广阔、异构且动态的技术领域。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
