AISP Toolkit Feb25 2025 - Flipbook - Page 45
Allowing data requesters to circumvent
established processes for accessing data,
whether intentionally or unintentionally.
Establishing a clear pricing structure for data
access and using it consistently (e.g., 昀氀at fee,
hourly rate for staff effort, discounts for studentor community-led projects).
Using a one-size-昀椀ts-all pricing model without
the input of governing bodies.
Finding legal pathways to share protected
data when it can be used to improve programs,
services, or people’s lives.
Hoarding high-value data under the guise of legal
restrictions or by limiting access only to those
with insider connections.
Sharing data to reduce administrative
burden on clients and communities (e.g.,
referral coordination, streamlined eligibility
determination).
Providing more data than is useful or necessary,
instead of curating and sharing variables based
on what is needed and of su昀케cient quality for the
project at hand.
Utilizing privacy-preserving technologies as a tool
to develop cross-agency relationships and build
trust.
Allowing privacy-preserving technologies to
replace participatory governance.
Incorporating con昀氀ict resolution approaches
within data governance policies to handle
disagreements regarding data access.
Refusing to share information with crucial
constituencies and community leaders (e.g.,
Tribal Nations, community organizers).
Unavailable Data
Clearly documenting why data are unavailable
(e.g., speci昀椀c statute, legislation, data quality
concerns, data not digitized, undue burden in data
preparation).
Storing potentially valuable data without creating
pathways for data access (i.e., data mausoleum
practices).
Utilizing privacy-preserving technologies to
mitigate risk in sharing and integrating protected
data, or even to allow for high-impact use cases of
sensitive data that would otherwise be classi昀椀ed
as unavailable.
Failing to consider and implement advancements
in technical tools that can enhance privacy and
security.
Protecting the rights of data owners to access
their data by building it into data management
practices and contracts.
Having data that are unavailable for technical
reasons rather than a legal or other legitimate
restriction on access.
CENTERING RACIAL EQUITY THROUGHOUT THE DATA LIFE CYCLE
Sharing information that helps requesters submit
a high-quality data request (e.g., metadata for
available datasets and variables, research/policy
priorities for data use, evaluation rubrics for data
requests).
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