Research Summary: Initial Evaluation of Potential Bias in Coverage of Humans in Wikidata

This research introduces an open-source auditing platform designed to evaluate potential biases in the representation of humans within Wikidata, a critical component for AI, information retrieval, and language modeling systems. The platform processes over 10 million statement bindings for more than 6 million human entities to assess demographic representation across multiple categories using statistical methods.

Why it matters

As artificial intelligence systems increasingly rely on open collaborative knowledge graphs like Wikidata, understanding and mitigating embedded biases in these foundational data sources is paramount. This research provides a tool for identifying systemic representational inequities, which is crucial for ensuring the fairness, accuracy, and ethical development of AI-driven applications and information systems globally.

Key insights

• A new open-source auditing platform has been developed to analyze demographic representation in Wikidata.
• The platform processes data for over 6 million human entities and 10 million statement bindings from Wikidata.
• Auditing covers gender, sexual orientation, geography, birthplace urbanicity, ethnicity, multilingual coverage, and occupation.
• The analysis also includes select intersectional pairs of these demographic entities.
• The evaluation methodology employs Chi-square goodness-of-fit tests and 95% Wilson-score intervals to assess representation.

Source

arXiv — Computers and Society — https://arxiv.org/abs/2609.22375