AISP Toolkit Feb25 2025 - Flipbook - Page 65
Positive and Problematic Practices:
Racial Equity in Reporting & Dissemination
POSITIVE PRACTICE
PROBLEMATIC PRACTICE
Producing one output that is inaccessible to
general audiences (e.g., a lengthy PDF report,
publication behind a paywall).
Using checklists to review products prior to
release for accessibility, cultural sensitivity,
nonstigmatizing language and visuals, usability,
and data integrity.
Disregarding how the 昀椀ndings and their
presentation may impact individuals or
communities (e.g., releasing data that stigmatizes
student subgroups, choosing graphic color
palettes inaccessible to the colorblind).
Developing tailored messaging for different
audiences that considers needs and preferences,
which may include the appropriate level of detail
and technical jargon, reading level, language,
length, and format.
Creating data visualizations or other products that
are di昀케cult to read, interpret, or make meaning of
for the people represented by the data.
Disseminating information that aims to improve
the lives of those represented in the data rather
than bring punitive action (e.g., analyzing food
purchase data to identify food deserts and guide
development of grocery stores vs. to remove
recipients from public bene昀椀ts).
Publishing data about de昀椀cits or what’s not
working without including the underlying social
context and suggestions for policy and practice
improvement.
Providing public access to aggregate data where
appropriate (e.g., dashboards, routine reports,
interactive maps).
Failing to include a clear description of the
underlying data used and necessary context for
interpretation.
Including stories as a complement to quantitative
昀椀ndings in order to better contextualize the lived
experience represented by the numbers.
Attempting to describe individual experiences
with aggregate or “whole population” data without
examining disparate impact based on race, gender,
and other intersections of identity.
Providing clear documentation of the data
analysis process along with analytic 昀椀les to ensure
replicability and reproducibility of results.
Making documentation indecipherable to those who
do not regularly work with the data and not including
contact information for those who have questions.
Giving proper credit to all individuals and groups
that supported the project, including co-authors,
community partners, data providers, funders,
reviewers, and participants.
Not giving attribution to the work or ideas of others
upon which the project builds.
Conducting impact analyses throughout the
project to assess: how does this work mitigate,
worsen, or ignore existing disparities?
Failing to respond to impact analyses, community
feedback, data errors, or harms identi昀椀ed in
reporting.
CENTERING RACIAL EQUITY THROUGHOUT THE DATA LIFE CYCLE
Creating a variety of products that communicate
昀椀ndings to different audiences through a range
of formats (e.g., static and interactive, digital and
analog).
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