从理论到实践:协调可信的人工智能分类法
Christos A Makridis1,2,3, Joshua Mueller1, Theo Tiffany1
1Department of Veterans Affairs, 810 Vermont Ave NW, Washington DC, 20001, United States.
Health policy OPEN
|November 5, 2024
概括
本研究评估了可信的人工智能 (AI) 框架,以帮助联邦机构制定定制的人工智能战略. 它提供了对人工智能价值观的整体观点,用于负责任的人工智能开发和部署.
科学领域:
- 人工智能治理管理 人工智能治理
- 人工智能道德和可靠性
- 公共部门 采用人工智能
背景情况:
- 人工智能能力的增加为组织带来了新的风险和脆弱性.
- 来自美国联邦机构和国际机构的众多值得信赖的AI框架存在.
- 对单一框架缺乏共识,阻碍了对人工智能的负责任实施.
研究的目的:
- 评估领先的可信的人工智能框架,并提供对人工智能价值观的整体视角.
- 为了使联邦机构能够创建特定机构可靠的AI策略.
- 为政府使用利用现有的人工智能原则提供一个框架.
主要方法:
- 对知名可信的人工智能框架的评估.
- 对可信的人工智能价值观的整体视角的发展.
- 该框架应用于退伍军人事务部 (VA) 的医疗保健系统.
主要成果:
- 确定需要与机构优先事项保持一致的机构特定AI策略.
- 为联邦机构展示了一种合成现有AI框架的方法.
- 提供了一个设计,开发,获取和在政府内部负责任地使用AI的基础.
结论:
- 联邦机构需要对可靠的人工智能的统一方法.
- 定制的人工智能策略可以在满足监管要求的同时促进信任和信心.
- 拟议的框架支持在政府运营中负责任地整合人工智能.
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