发现维基数据中职业性别与年龄偏见，南北差异明显。
Social Biases in Knowledge Representations of Wikidata separates Global North from Global South
- 用公平性指标审计知识图谱中的链接预测偏差
- 21个地理区域数据显示性别/年龄偏见显著分隔全球南北
- 适合关注算法公平性与数据伦理的研究者阅读
知识图谱在信息检索、聊天机器人和语言模型等下游应用中广泛应用，但其自动构建过程可能引入社会偏见。本文提出审计框架AuditLP，通过公平性度量检测链接预测中的偏见，聚焦性别（男性/女性主导）和年龄（青年/老年/中立）敏感属性。在来自开源知识图谱Wikidata的21个不同地理区域的大规模知识三元组上进行实验，结果表明：偏见程度在地理分布上清晰映射出全球社会经济与文化分野，形成明显的全球北方与全球南方隔离格局。
原文摘要 · Abstract (English)
Knowledge Graphs have become increasingly popular due to their wide usage in various downstream applications, including information retrieval, chatbot development, language model construction, and many others. Link prediction (LP) is a crucial downstream task for knowledge graphs, as it helps to address the problem of the incompleteness of the knowledge graphs. However, previous research has shown that knowledge graphs, often created in a (semi) automatic manner, are not free from social biases. These biases can have harmful effects on downstream applications, especially by leading to unfair behavior toward minority groups. To understand this issue in detail, we develop a framework -- AuditLP -- deploying fairness metrics to identify biased outcomes in LP, specifically how occupations are classified as either male or female-dominated based on gender as a sensitive attribute. We have experimented with the sensitive attribute of age and observed that occupations are categorized as young-biased, old-biased, and age-neutral. We conduct our experiments on a large number of knowledge triples that belong to 21 different geographies extracted from the open-sourced knowledge graph, Wikidata. Our study shows that the variance in the biased outcomes across geographies neatly mirrors the socio-economic and cultural division of the world, resulting in a transparent partition of the Global North from the Global South.
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