用AI平台日志测职业AI暴露存在严重偏差，需重加权才准。
Who Uses AI? Platform Selection and the Measurement of Occupational AI Exposure
- 用平台用户数据测职业AI使用，结果受用户构成影响
- 同一厂商不同渠道的结论可能相反，误差可达1.9倍
- 按真实劳动力结构重加权，估计值下降42%至93%
AI平台的对话日志被广泛用于衡量职业层面的人工智能接触程度，但这些日志中的用户并非真实工作人群。我们发现，平台推导出的暴露分数同时受任务层级的AI适用性与平台用户群体的职业构成影响。在保持实证设计不变的前提下，仅更换平台输入，即可使后ChatGPT时期就业系数变化达1.9倍；同一供应商的消费者与企业渠道甚至在符号上产生分歧。我们形式化了这种非经典测量误差，将其分解为职业间与职业内选择效应，并构建了基于劳动力统计权重的局部识别边界。将权重调整至美国劳工统计局的就业占比后，估计值被削弱42%至93%。该偏差更直接反映了观察用户中AI的增强效应，而非劳动力中的替代效应。
原文摘要 · Abstract (English)
Conversation logs from AI platforms are increasingly used to measure occupational exposure to artificial intelligence, but the users observed in these logs are not the workforce. We show that platform-derived exposure scores combine task-level AI applicability with the occupational composition of the platform's user base. Holding the empirical design fixed, changing only the platform input changes the post-ChatGPT employment coefficient by a factor of 1.9, and consumer and enterprise channels within the same vendor disagree in sign. We formalize the resulting non-classical measurement error, decompose it into between- and within-occupation selection, and construct workforce-reweighted partial-identification bounds. Reweighting to Bureau of Labor Statistics employment shares attenuates estimates by 42 to 93 percent. The bias captures augmentation among observed users more directly than substitution in the workforce.
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