风险分析的人工智能 - - 一个风险表征的视角,对进步,机遇和局限性
Kaia Stødle1, Roger Flage1, Seth Guikema2
1Department of Safety, Economics and Planning, University of Stavanger, Stavanger, Norway.
概括
人工智能 (AI) 提供了风险分析的新方法,将其定义为输入-算法-输出过程. 然而,人工智能不能完全自动化风险分析和决策,因为对不确定性和风险知情过程的关键担忧.
科学领域:
- 风险分析和人工智能 (AI).
- 开发新的AI驱动的风险评估方法.
- 人工智能在科学研究中的跨学科应用.
背景情况:
- 人工智能 (AI) 为推进风险分析提供了重大机会.
- 目前的风险分析方法可以通过AI集成来增强.
- 需要改进后果和不确定性表征的模型.
研究的目的:
- 将AI在风险分析中的应用概念化为输入-算法-输出过程.
- 将AI框架与风险描述中的关键任务联系起来:后果,不确定性和知识管理.
- 探索AI在风险分析中的当前和未来用途,并讨论自动化限制.
主要方法:
- 使用输入算法输出框架对风险分析进行AI的概念化.
- 审查当前基于AI的风险分析概念和方法.
- 对未来风险分析成果和决策支持的AI能力进行推断.
主要成果:
- 人工智能可以在风险分析中得到比目前更广泛的应用.
- 人工智能框架可以支持后果特征,不确定性特征和知识管理.
- 当前的AI应用为未来更先进的风险分析工具提供了基础.
结论:
- 有机会增加AI在风险分析中的使用.
- 关于不确定性表示的关键问题需要仔细的AI实施.
- 风险分析和决策的完全自动化是不可行的;人类监督对于基于风险的决策至关重要.
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