Public AI incident database counts conflate changes in reporting propensity, deployment growth, and shifts in harm frequency per unit of exposure. These issues introduce significant uncertainties challenging public and corporate policy frameworks centred on realized risks. We propose a simple framework that establishes clear points of inquiry, separately estimates exposure from harm-rate trends, and then classifies into meaningful trajectory categories for governance decisions. The framework combines a structured monitoring question format (SORT) to clarify coverage decisions, a tiered estimation procedure calibrated to available evidence, and LLM-assisted incident matching against public databases. Applied to various monitoring questions, we draw conclusions regarding the monitoring ecosystem more broadly: Providing an essential interpretative classification, determining what can and cannot be claimed, and establishing that exposure estimation is required as AI deployments become increasingly common.

翻译：公共人工智能事件数据库统计混淆了报告倾向的变化、部署增长及单位暴露下伤害频率的变迁。这些问题引入了显著的不确定性，挑战了以已实现风险为中心的公共与企业政策框架。我们提出一个简单框架，确立清晰的探究路径，分别估算暴露与伤害率趋势，并将其分类为有意义的治理决策轨迹类别。该框架结合结构化监测问题格式（SORT）以明确覆盖范围决策、依据可用证据校准的分级估算程序，以及利用大语言模型辅助的事件与公共数据库匹配。应用于各类监测问题时，我们从更广泛的监测生态系统中得出结论：提供必要的解释性分类，确定可主张与不可主张的内容，并明确随着人工智能部署日益普及，暴露估算是必需的。