AISP Toolkit Feb25 2025 - Flipbook - Page 26
Positive and Problematic Practices: Planning
POSITIVE PRACTICE
PROBLEMATIC PRACTICE
CENTERING RACIAL EQUITY THROUGHOUT THE DATA LIFE CYCLE
Individual and Organizational Readiness
Engaging in individual education and selfre昀氀ection around personal identities, implicit
bias, racism, power, and oppression.
Assuming that data are neutral, that individual
identity does not in昀氀uence data use, and that
analyses can be conducted free of bias.
Ensuring alignment between mission, vision,
guiding principles, and organizational policies
around employee bene昀椀ts and well-being.
Not acknowledging the labor required in this 昀椀eld
and encouraging burnout practices.
Developing organizational understanding of
sociological perspectives along with strategies to
center racial equity.
Moving projects through the data life cycle before
understanding the broader social, historical, and
political context of data access and use and the
organization’s place in the 昀椀eld.
Building relationships between community,
administrators, researchers, and technologists
over time so that they are prepared to work
together collaboratively and effectively.
Using deadlines or grant deliverables as an excuse
to avoid investing in relationships.
Preparing community members and formally
trained researchers to take an active role in
data projects and providing opportunities for all
participants to speak, listen, and build knowledge.
Failing to demystify data and maintaining the false
idea that working with data is only for those with
specialized training.
Team Formation and Governance
Creating participatory data governance that
intentionally involves a diversity of perspectives
and skillsets—community members, subject
matter experts, agency staff, methodologists, etc.
Using only token “representation” in data
governance processes.
Setting clear mission, vision, and guiding
principles to ground data governance processes
and formalize the role of community voice,
oversight, and ownership in decision-making.
Refusing to cede power and be accountable to the
community whose data are being used.
Sta昀케ng the data effort with people who re昀氀ect
the population or jurisdiction the data represent.
Not compensating people who make meaningful
contributions of their lived experience,* knowledge,
time, or skills.
* It is important to note that we all have lived experiences, and the term “lived experience” should be relevant to the topic of
study and not used as coded language to imply marginalized identity.
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