基于群体特定姓名列表的概率性种族与民族预测

中文摘要 AI 辅助

对种族和民族差异进行统计上有效的估计，通常需要仅根据个体的姓名和地理位置来推断其属于特定种族或民族群体的概率。标准方法——贝叶斯改进姓氏地理编码（BISG）——依赖于每个姓名的群体人口频率。尽管美国人口普查局为常见姓名和有限的种族类别提供了此类信息，但许多种族和民族群体缺乏可比数据，且此类数据在美国以外地区很少可用。我们提出了列表驱动的BISG（ℓBISG）方法，该方法可用于从群体特定姓名列表中推导出校准的群体概率。这些列表可基于专家知识编制，或使用大型语言模型（LLMs）合成生成，因此可能受到未知偏差的影响。将姓名表示为嵌入，我们将列表成员资格视为代理预测任务，并应用基于邻近推断的校正来恢复目标群体概率。我们在具有自我报告种族的美国选民档案、1900年美国全面人口普查以及黎巴嫩选民登记册上验证了该方法。我们发现，LLM生成的姓名列表能产生准确且校准良好的概率，以及精确的差异估计，与使用需要姓名-种族数据的方法所得结果相当。因此，ℓBISG显著扩展了概率性种族和民族预测在无法获得姓名-种族数据场景中的适用性。

英文摘要

Statistically valid estimation of racial and ethnic disparities often requires inferring the probability that an individual belongs to a particular racial or ethnic group given only their name and geographic location. The standard approach, Bayesian Improved Surname Geocoding (BISG), relies on group population frequencies for each name. Although the U.S. Census Bureau provides such information for common names and a limited set of racial categories, comparable data do not exist for many racial and ethnic groups and are rarely available outside the U.S. We propose the list-powered BISG ($\ell$BISG) method, which can be used to derive calibrated group probabilities from group-specific name lists. These lists may be compiled based on expert knowledge or generated synthetically using large language models (LLMs), and thus may be subject to unknown biases. Representing names as embeddings, we treat list membership as a proxy prediction task and apply a correction based on proximal inference to recover the target group probabilities. We validate the method on U.S. voter files with self-reported race, on the full-count 1900 U.S. Census, and on the Lebanese voter registry. We find that LLM-generated name lists yield accurate and well-calibrated probabilities as well as precise disparity estimates comparable to those obtained using methods that require name-race data. Thus, $\ell$BISG substantially broadens the applicability of probabilistic race and ethnicity prediction to settings where name-race data are unavailable.

发表机构

• Harvard University（哈佛大学）

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