带绳,而不是护:以管理为基础的人工智能风险监管方法
Cary Coglianese1, Colton R Crum2
1Law School, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
概括
规范人工智能 (AI) 需要灵活的"绳索",而不是硬的"护". 基于管理的监管,在人类监督下,提供了一种可适应的方法,有效地管理AI风险.
科学领域:
- 人工智能治理 AI治理
- 技术法规 技术法规 技术法规
- 风险管理 风险管理
背景情况:
- 越来越多的人呼吁对人工智能 (AI) 进行监管,以防止潜在的危害.
- 传统的"护"监管模式被认为不适合人工智能的动态和异质性质.
- 不负责任的AI开发和使用带来了重大风险,需要监管监督.
研究的目的:
- 反对对人工智能的规定性"护"法规.
- 作为更有效的人工智能治理策略,提出"带" - - 具体是基于管理的监管.
- 探索对人工智能风险治理的基于管理的监管的实施.
主要方法:
- 概念分析比较"护"和"绳索"的监管方法.
- 检查基于管理的监管作为灵活的监管模式.
- 讨论现有的监管工作与基于管理的元素.
主要成果:
- 由于AI的动态和异质性,严格的规范性法规 ("护") 不适合人工智能.
- 灵活,可适应的"绳索",以管理为基础的监管为例,被提出为一个优越的替代方案.
- 基于管理的监管需要持续的人类监督,以有效地管理AI风险.
结论:
- 基于管理的监管为管理人工智能风险提供了比传统护更有效和更适应的框架.
- "带"方法,强调人类的监督和灵活性,对于负责任的AI开发和部署至关重要.
- 需要进一步的研究来解决基于管理的AI监管的实施挑战.
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