The Gendered Geography of Contributions to OpenStreetMap: Complexities in Self-Focus Bias

Millions of people worldwide contribute content to peer production repositories that serve human information needs and provide vital world knowledge to prominent artificial intelligence systems. Yet, extreme gender participation disparities exist in which men significantly outnumber women. A central concern has been that due to self-focus bias [46], these disparities can lead to corresponding gender content disparities, in which content of interest to men is better represented than content of interest to women. This paper investigates the relationship between participation and content disparities in OpenStreetMap. We replicate findings that women are dramatically under-represented as OSM contributors, and observe that men and women contribute different types of content and do so about different places. However, the character of these differences confound simple narratives about self-focus bias: we find that on a proportional basis, men produced a higher proportion of contributions in feminized spaces compared to women, while women produced a higher proportion of contributions in masculinized spaces compared to men. We discuss the implications of these complex results for both theory and practice.