AI 中文总结
本文针对国家统计机构，提出需通过独立统计验证和保护受访者保密性确保AI算法可信，开发Mini Max Hierarchical Bayes抽样算法，在合成劳动力人口及澳大利亚普查数据上分别实现80%、90%样本量减少，给出符合国际框架的评估清单。
AI 中文摘要
为在预算收紧的情况下满足对细分人口统计和社会经济指标日益增长的需求，国家统计机构必须持续开发新方法，包括利用大数据、卫星图像和交易来源来改进或重新设计数据收集。人工智能可为这项工作提供支持，但未经严格验证的人工智能生成算法不得用于生产环节。本文聚焦官方统计中使用人工智能的两项信任基础：生产使用前的独立统计验证，以及开发与测试期间对受访者保密性的严格保护。作者通过指导人工智能构建并实施Mini Max Hierarchical Bayes抽样算法的经验来说明该方法：将其应用于合成劳动力人口时，经1000次重复的蒙特卡洛研究确认，该方法达到所有规定精度目标，同时将所需样本量减少80%；应用于2021年澳大利亚人口普查微观数据时，实现90%的样本量减少，且产生的全国点估计值准确度远低于1%。本文最后给出一份符合《联合国官方统计基本原则》及HLG MOS统计算法质量框架的实用评估清单。
英文摘要
To meet growing demand for granular demographic and socioeconomic indicators under tighter budgets, national statistical offices must continually develop new methods. These include using big data, satellite imagery, and transactional sources to improve or redesign data collection. Artificial intelligence can support this work, but algorithms generated with AI should not be trusted for production without rigorous verification. This paper focuses on two foundations of trust in the use of AI in official statistics: independent statistical verification before production use, and disciplined protection of respondent confidentiality during development and testing. The approach is illustrated through the author's experience directing AI to construct and implement a Mini Max Hierarchical Bayes sampling algorithm. Applied to a synthetic labour force population, the method met all specified precision targets while reducing the required sample size by 80 percent, as confirmed by a Monte Carlo study with 1000 replications. Applied to 2021 Australian Census microdata, it achieved a 90 percent reduction while producing national point estimates accurate to well below 1 percent. The paper concludes with a practical evaluation checklist aligned with the UN Fundamental Principles of Official Statistics and the HLG MOS Quality Framework for Statistical Algorithms.
Comments18 pages
