Regulating Autonomous and Agentic AI: Challenges and Solutions
Score
81系统对比四大监管框架，揭示了自主AI监管的共性盲区，填补了现有研究对供应链责任分散和前瞻性监管的讨论空白。
案例详实，逻辑严谨，从现象到建议层层递进，但部分假设缺乏实际监管数据验证。
直接为监管机构和企业提供可操作的框架转向建议，尤其对政策制定者具有高参考价值。
2025年最新论文，正值全球AI监管立法密集期，议题高度契合当前热点。
来自arXiv预印本，但引用文献涵盖顶级期刊与官方文件，权威性较高；尚未经同行评审需谨慎引用。
Base 89.5 × T2 Source weight ×0.9 = 81
Curation line 70Selected
Why selected
Rich and representative case studies systematically dissect the regulatory dilemma of autonomous AI, though non-Western regimes are not covered.
AI commentary
「This paper is a must-read for anyone interested in AI governance, as it is the first to clearly explain how 'autonomy' as a technical feature dismantles traditional regulatory logic. For legislators drafting AI laws and legal teams in tech companies building compliance systems, it offers a complete roadmap from 'ex-post fines' to 'ex-ante prevention.'」
Quick overview
This paper examines the regulatory challenges posed by autonomous and agentic AI, where traditional assumptions about regulatee knowledge and control break down due to distributed responsibility across the AI supply chain. It studies four regulatory systems: UK content platform regulation, data protection, UK financial services, and the EU AI Act. The paper identifies systemic risks and the ineffectiveness of retrospective oversight, proposing a shift from reactive to proactive regulation to adapt to AI autonomy.
- Autonomous AI undermines regulatory assumptions: regulators no longer have full knowledge or control, with responsibility diffused across the AI supply chain.
- Analyzes common challenges across four regimes: UK content platforms, data protection, UK financial services, and the EU AI Act.
- Retrospective oversight fails as a risk management tool, necessitating proactive, forward-looking regulatory frameworks.
- Proposes bringing the entire AI supply chain into regulatory scope and addressing new systemic risks from AI autonomy.
Full text
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