  Emerging discussions on the responsible government use of algorithmic
technologies propose transparency and public participation as key mechanisms
for preserving accountability and trust. But in practice, the adoption and use
of any technology shifts the social, organizational, and political context in
which it is embedded. Therefore translating transparency and participation
efforts into meaningful, effective accountability must take into account these
shifts. We adopt two theoretical frames, Mulligan and Nissenbaum's handoff
model and Star and Griesemer's boundary objects, to reveal such shifts during
the U.S. Census Bureau's adoption of differential privacy (DP) in its updated
disclosure avoidance system (DAS) for the 2020 census. This update preserved
(and arguably strengthened) the confidentiality protections that the Bureau is
mandated to uphold, and the Bureau engaged in a range of activities to
facilitate public understanding of and participation in the system design
process. Using publicly available documents concerning the Census'
implementation of DP, this case study seeks to expand our understanding of how
technical shifts implicate values, how such shifts can afford (or fail to
afford) greater transparency and participation in system design, and the
importance of localized expertise throughout. We present three lessons from
this case study toward grounding understandings of algorithmic transparency and
participation: (1) efforts towards transparency and participation in
algorithmic governance must center values and policy decisions, not just
technical design decisions; (2) the handoff model is a useful tool for
revealing how such values may be cloaked beneath technical decisions; and (3)
boundary objects alone cannot bridge distant communities without trusted
experts traveling alongside to broker their adoption.
