发表机构
Institute for Health Equity Research (IHER); Icahn School of Medicine at Mount Sinai (ISMMS)（健康公平研究所（IHER）; 西奈山伊坎医学院（ISMMS））
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出一个开源审计平台，基于维基数据超600万人类条目，评估性别、族裔等多维度代表性，发现女性占28.71%，族裔与性取向记录缺失最严重，农村出生地与非WENA公民身份代表性最低。
AI 中文摘要
引言。开放协作知识图谱（如维基数据）日益为智能体人工智能、信息检索和语言建模系统提供基础支撑，这使得对其人口统计代表性及整体公平性进行系统性审计成为一项研究要务。方法。本文中，我们提出一个开源审计平台，该平台通过QLever摄取维基数据上代表超过600万人类实体的逾1000万条语句绑定，并评估性别、性取向、地理、出生地城市化程度、族裔、标签、描述和别名的多语言覆盖、职业以及这些实体中若干交叉组合对的代表性。该平台利用卡方拟合优度检验、95%威尔逊得分置信区间和差异比率，并参照鲁宾缺失数据分类法进行分析。结果。在维基数据中具有明确性别的人类实体中，女性占28.71%（置信区间±0.04）。38.26%的人类实体具有公民身份声明，其中西欧和北美（WENA）约占此类声明的53%。在可分类的180万个出生地中，农村出生地占2.48%（而全球基线为27.4%）。不到1.2%的实体带有族裔声明，非英语维基数据描述覆盖了18.2%的条目。讨论。我们的研究结果揭示了所评估各轴线上显著的缺失性。族裔和性取向是最为严重记录不足（缺失声明）的类别，而农村出生地和非WENA公民身份则是最未被充分代表的类别。
英文摘要
Introduction. Open collaborative knowledge graphs such as Wikidata increasingly ground agentic artificial intelligence, information retrieval, and language modeling systems, making systematic auditing of their demographic representation and overall equity a research imperative. Methods. Herein, we present an open-source auditing platform that ingests over 10 million statement bindings representing over 6 million humans on Wikidata via QLever, and evaluates representation of gender, sexual orientation, geography, birthplace urbanicity, ethnicity, multilingual coverage of labels, descriptions, and aliases, occupation, and select intersectional pairs of these entities. It does so by making use of Chi-square goodness-of-fit tests, 95% Wilson-score confidence intervals, and disparity ratios, in light of Rubin's missingness taxonomy. Results. Women accounted for 28.71% (CI +/-0.04) of all humans in Wikidata with a stated gender. 38.26% of humans had a citizenship statement, with Western Europe and North America (WENA) representing approximately 53% of such statements. Among 1.8 million birthplaces that could be classified, rural birthplaces were observed in 2.48% of cases (in comparison to 27.4% global baseline). Fewer than 1.2% of entities carried an ethnicity statement, and non-English Wikidata descriptions covered 18.2% of items. Discussion. Our findings reveal significant missingness across the evaluated axes. Ethnicity and sexual orientation were the most critically under-documented (missing statements) while rural birthplaces and non-WENA citizenship were the most underrepresented.
Comments11 pages, 3496 words, 1 table, 5 figures
