政府应推动AI部署后跨系统监测，以识别真实影响。
The Role of Governments in Increasing Interconnected Post-Deployment Monitoring of AI
- 构建模型使用、应用与事件的联动监测体系
- 结合推理时间与长期行业扩散数据追踪风险
- 适合政策制定者与监管机构参考
语言类AI系统正广泛融入社会，带来正面与负面效应。减轻负面影响依赖于基于实证证据的准确影响评估，需建立AI使用与实际影响之间的因果联系。互联的部署后监测整合模型集成与使用、应用情况及事件与影响信息。例如，可将链式思维推理的推理时间监控与行业层面的AI扩散、影响和事件的长期监控相结合。借鉴其他行业的信息共享机制，本文提出政府可收集的具体数据来源与关键数据点，以支持人工智能风险管理。
原文摘要 · Abstract (English)
Language-based AI systems are diffusing into society, bringing positive and negative impacts. Mitigating negative impacts depends on accurate impact assessments, drawn from an empirical evidence base that makes causal connections between AI usage and impacts. Interconnected post-deployment monitoring combines information about model integration and use, application use, and incidents and impacts. For example, inference time monitoring of chain-of-thought reasoning can be combined with long-term monitoring of sectoral AI diffusion, impacts and incidents. Drawing on information sharing mechanisms in other industries, we highlight example data sources and specific data points that governments could collect to inform AI risk management.
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