AI Contextual Measurement for Recovering Individual and Group-Level Effects: Validation Against Survey Measures and an Occupational Application· AI上下文测量框架
Researchers increasingly use artificial intelligence to construct measures of social, organizational, and occupational characteristics that are absent from conventional surveys. We propose AICOME, AI COntextual MEasurement, a framework for evaluating whether AI-derived respondent-level measures can recover individual and group-level effects in contextual models. The key idea is that an AI measure constructed at the respondent level can be used to derive its group-level aggregate and its individual deviation, allowing researchers to estimate both between-group and within-group associations rather than treating AI measurement as response prediction alone. We validate the framework using the 2022 China Family Panel Studies (CFPS), where occupations provide the empirical grouping structure and
AI上下文测量框架评估AI衍生度量在情境模型中恢复个体和群体效应的能力。
- 核心方法
- 提出AICOME框架，通过AI在个体层面构建度量，推导出群体层面的聚合值及其个体差异，从而估计群体间和群体内的关联。
- 适合谁读
- 适合社会科学研究者、AI研究者和数据科学家阅读
- 要解决的问题
- 传统调查中缺乏有效的社会、组织和职业特征度量，导致难以准确评估这些特征对个体和群体的具体影响。
- 关键实验
- 使用2022年中国家庭追踪调查（CFPS）数据，以职业作为实证分组结构进行验证。
- 主要贡献
- 提供了一种系统的方法来验证AI衍生度量的有效性，能够在情境分析中同时考虑个体和群体层面的影响。
- 意义与局限
- 意义在于提高AI在社会科学研究中的应用价值，影响是可能改变传统指标构建方式，局限在于依赖特定数据集和模型假设。