ABSTRACT
This chapter, reprinted from Data Feminism, emphasizes the importance of pluralism in data science, arguing that comprehensive knowledge emerges from synthesizing diverse perspectives, particularly local, Indigenous, and experiential insights. It critiques the glamorization of data scientists as “rock stars” or “wizards” who alone can manage data, pointing out that such metaphors are not only misleading but also perpetuate gender and racial stereotypes. A key example discussed is the Anti-Eviction Mapping Project (AEMP) in San Francisco, which employs a collaborative and inclusive approach to data visualization to highlight the city’s eviction crisis. This chapter also highlights the pitfalls of over-prioritizing data cleanliness and control, suggesting that this can obscure the richness and context of the original data, a concept linked to historical eugenics movements. Ultimately, this chapter calls for a shift from the “data for good” model, which can often be paternalistic, to a “data for co-liberation” model, emphasizing community-led initiatives, knowledge transfer, and building social infrastructure, ensuring that data projects empower marginalized groups and foster solidarity.
