The article discusses the U.S. Census Bureau's adoption of differential privacy, emphasizing the complexities involved in fostering transparent and participatory algorithmic governance. It underscores the importance of mediating handoffs among stakeholders with varying expertise, utilizing boundary objects to facilitate meaningful collaboration. The findings stress that lessons from this experience are relevant beyond Census practices, informing broader algorithmic adoption initiatives in government and the intricacies of promoting transparency and accountability in this context.
The adoption of differential privacy by the U.S. Census Bureau marked a pivot in their practices around transparent and participatory algorithmic governance, revealing complex stakeholder dynamics.
The lessons learned from the Census Bureau's experience with differential privacy are essential for understanding the broader implications of algorithmic adoption in government sectors.
Collection
[
|
...
]