Beyond 'Geo' HCI: Exploring Cultural Dimensions of Disparity in OpenStreetMap Road Safety Metadata

Peer production systems like OpenStreetMap exhibit persistent information gaps across socioeconomic and population density lines, posing challenges for end users, especially as AI tools such as autonomous vehicles rely on this incomplete data. While prior work has established these trends, their impact on the production of critical semantic metadata remains unclear. In this study, we focus on three OpenStreetMap metadata tags that are essential for supporting road safety. Contrary to the expected socioeconomic and population density trends, our findings reveal that cultural factors play a significant role in influencing tag production. Moreover, we find that automated contributions may hinder human tag production in OpenStreetMap, despite the potential of these automated imports to address data gaps. Overall, our results add nuance to the understanding of the trade-offs involved in automated imports and shed light on how both practitioners and the public can more effectively improve metadata coverage.