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.

翻译：基于语言的人工智能系统正在社会扩散，带来积极与消极影响。减轻负面影响依赖于准确的影响评估，这需要建立能够证明人工智能使用与影响间因果关系的实证证据基础。互联的部署后监控整合了模型集成与使用、应用使用、事件与影响等多维度信息。例如，可将思维链推理的实时推理监控与行业人工智能扩散、影响及事件的长期监测相结合。借鉴其他行业的信息共享机制，我们列举了政府为支持人工智能风险管理可收集的示例数据源及具体数据点。