Differential Perspectives: Epistemic Disconnects Surrounding the US Census Bureau's Use of Differential Privacy

Abstract

When the U.S. Census Bureau announced its intention to modernize its disclosure avoidance procedures for the 2020 Census, it sparked a controversy that is still underway. The move to differential privacy introduced technical and procedural uncertainties, leaving stakeholders unable to evaluate the quality of the data. More importantly, this transformation exposed the statistical illusions and limitations of census data, weakening stakeholders' trust in the data and in the Census Bureau itself. This essay examines the epistemic currents of this controversy. Drawing on theories from Science and Technology Studies (STS) and ethnographic fieldwork, we analyze the current controversy over differential privacy as a battle over uncertainty, trust, and legitimacy of the Census. We argue that rebuilding trust will require more than technical repairs or improved communication; it will require reconstructing what we identify as a 'statistical imaginary.'

References in corpus (2)

• The Impact of the U.S. Census Disclosure Avoidance System on Redistricting and Voting Rights Analysis
• Census TopDown: The Impacts of Differential Privacy on Redistricting

Cited by in corpus (6)

• Don't Look at the Data! How Differential Privacy Reconfigures the Practices of Data Science
• Evaluating Bias and Noise Induced by the U.S. Census Bureau's Privacy Protection Methods
• Comment: The Essential Role of Policy Evaluation for the 2020 Census Disclosure Avoidance System
• Statistical Imaginaries, State Legitimacy: Grappling with the Arrangements Underpinning Quantification in the U.S. Census
• What to Consider When Considering Differential Privacy for Policy
• General Inferential Limits Under Differential and Pufferfish Privacy