  In "Differential Perspectives: Epistemic Disconnects Surrounding the US
Census Bureau's Use of Differential Privacy," boyd and Sarathy argue that
empirical evaluations of the Census Disclosure Avoidance System (DAS),
including our published analysis, failed to recognize how the benchmark data
against which the 2020 DAS was evaluated is never a ground truth of population
counts. In this commentary, we explain why policy evaluation, which was the
main goal of our analysis, is still meaningful without access to a perfect
ground truth. We also point out that our evaluation leveraged features specific
to the decennial Census and redistricting data, such as block-level population
invariance under swapping and voter file racial identification, better
approximating a comparison with the ground truth. Lastly, we show that accurate
statistical predictions of individual race based on the Bayesian Improved
Surname Geocoding, while not a violation of differential privacy, substantially
increases the disclosure risk of private information the Census Bureau sought
to protect. We conclude by arguing that policy makers must confront a key
trade-off between data utility and privacy protection, and an epistemic
disconnect alone is insufficient to explain disagreements between policy
choices.
