人工智能风险评估中的科学和实践比例性
Carlos Mougan1, Lauritz Morlock1, Jair Aguirre2
1European AI Office, European Commission, Brussels, Belgium.
概括
人工智能评估必须提供有用的风险洞察力,而不会造成不必要的工作. 这确保了人工智能风险评估对所有用户来说都是实用和有效的.
科学领域:
- 人工智能的人工智能
- 风险管理 风险管理
- 评估方法的评估方法.
背景情况:
- 人工智能 (AI) 的日益集成需要强大的评估框架.
- 当前的人工智能评估方法可能会造成重大负担,阻碍广泛采用和实际应用.
- 需要人工智能风险评估策略,以平衡彻底性和效率.
研究的目的:
- 探索人工智能评估的方法,从而获得有意义的风险信息.
- 调查可以尽量减少与AI风险评估相关的负担的方法.
- 为评估人工智能系统提出一个平衡的框架.
主要方法:
- 审查现有的AI评估协议.
- 分析AI中的风险沟通策略.
- 开发一个概念模型,以有效地评估AI风险.
主要成果:
- 确定了AI有意义的风险信息的关键组成部分.
- 在当前的人工智能评估过程中描述了常见的负担.
- 提出了一种简化方法,重点关注具有高影响力的风险指标.
结论:
- 有效的AI评估可以提供关键的风险见解.
- 尽量减少评估负担对于实际的AI部署至关重要.
- 一种平衡的方法可以提高AI风险管理的实用性和可行性.
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