Analyzing Race and Country of Citizenship Bias in Wikidata

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 additional data about STEM scientists from the minorities. This data is ready to be inserted into Wikidata with a bot. Increasing representation of minority race and country of citizenship groups can create a more accurate portrayal of individuals in STEM.