Analyzing Race and Country of Citizenship Bias in Wikidata
Zaina Shaik, Filip Ilievski, Fred Morstatter

TL;DR
This paper investigates biases related to race and country of citizenship in Wikidata, revealing overrepresentation of white individuals and those from Europe and North America, and proposes data augmentation to improve diversity in STEM representation.
Contribution
It provides a comprehensive analysis of race and citizenship biases in Wikidata and introduces a method to enhance minority representation through data linking and bot insertion.
Findings
Overrepresentation of white individuals in Wikidata
European and North American citizenship groups are overrepresented
Minority data was linked and prepared for insertion into Wikidata
Abstract
As an open and collaborative knowledge graph created by users and bots, it is possible that the knowledge in Wikidata is biased in regards to multiple factors such as gender, race, and country of citizenship. Previous work has mostly studied the representativeness of Wikidata knowledge in terms of genders of people. In this paper, we examine the race and citizenship bias in general and in regards to STEM representation for scientists, software developers, and engineers. By comparing Wikidata queries to real-world datasets, we identify the differences in representation to characterize the biases present in Wikidata. Through this analysis, we discovered that there is an overrepresentation of white individuals and those with citizenship in Europe and North America; the rest of the groups are generally underrepresented. Based on these findings, we have found and linked to Wikidata…
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Taxonomy
TopicsWikis in Education and Collaboration · Natural Language Processing Techniques · Social Media and Politics
