# Analyzing Race and Country of Citizenship Bias in Wikidata

arXiv: https://arxiv.org/abs/2108.05412 · HTML: https://mlsanity.com/paper/2108.05412/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 7 references that are in the map.
- First posted on arXiv 11 Aug 2021.

## Builds on (most cited first, 3 of 3)

- [Measuring Social Bias in Knowledge Graph Embeddings](https://mlsanity.com/paper/1912.02761/) (2019)
- [KGTK: A Toolkit for Large Knowledge Graph Manipulation and Analysis](https://mlsanity.com/paper/2006.00088/) (2020)
- [Bias in Conversational Search: The Double-Edged Sword of the Personalized Knowledge Graph](https://mlsanity.com/paper/2010.10409/) (2020)

## Built on by (most cited first, 6 of 6)

- [Evaluating Language Models for Knowledge Base Completion](https://mlsanity.com/paper/2303.11082/) (2023)
- [Social Biases in Knowledge Representations of Wikidata separates Global North from Global South](https://mlsanity.com/paper/2505.02352/) (2025)
- [Factual Inconsistencies in Multilingual Wikipedia Tables](https://mlsanity.com/paper/2507.18406/) (2025)
- [Hidden Author Bias in Book Recommendation](https://mlsanity.com/paper/2209.00371/) (2022)
- [Per-Entity Bias Mapping for AI Visibility: Why Brand Mentions Require Entity-Specific Calibration](https://mlsanity.com/paper/2606.21595/) (2026)
- [Initial Evaluation of Potential Bias in Coverage of Humans in Wikidata](https://mlsanity.com/paper/2609.22375/) (2026)

## Abstract (arXiv)

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.