监管先进的人工制剂
Michael K Cohen1,2, Noam Kolt3,4, Yoshua Bengio5,6
1University of California, Berkeley, CA, USA.
概括
需要新的治理框架来管理可能带来安全风险且无法测试的人工智能系统. 这项研究突显了对可适应人工智能治理的必要性, 以确保公共安全.
科学领域:
- 计算机科学
- 人工智能道德
- 公共政策
背景情况:
- 人工智能 (AI) 的快速发展带来了前所未有的社会利益和风险.
- 目前的人工智能治理框架主要依赖于测试和验证,这对于复杂或不可预测的人工智能系统来说可能不足.
- 确保人工智能的安全性和可靠性至关重要,因为它将越来越多地融入关键基础设施和决策过程.
研究的目的:
- 探索无法安全测试的人工智能系统带来的挑战.
- 提出必要的治理框架调整以应对这些挑战.
- 确保人工智能技术的负责任开发和部署.
主要方法:
- 对现有的人工智能治理模型和安全测试方法的文献审查.
- 对抗传统安全评估的理论AI系统的分析.
- 为无法测试的人工智能开发概念治理原则.
主要成果:
- 特定的AI特征 (例如,新出现的行为,黑盒性质) 排除了安全测试.
- 建议从纯粹的经验安全验证转向更强有力的基于原则的治理方法.
- 强调需要持续监测和适应性监管战略.
结论:
- 现有的人工智能治理框架对于无法安全测试的系统是不够的.
- 未来的治理必须包括积极的风险管理和对人工智能的适应性监督.
- 解决未经测试的AI挑战对于保持公众对AI部署的信任和安全至关重要.
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