AISP Toolkit Feb25 2025 - Flipbook - Page 25
(see Finding a Way Forward) and considering duty of care;11 developing understanding of the local
racial, social, and historical context; and creating processes to evaluate risk vs. bene昀椀t for each
project or data use. It is a common mistake to let the forward momentum and looming deadlines at
the outset of a data project overshadow upfront (often internal) equity work. Incorporating a racial
equity lens during the planning stage sets the foundation for embedding racial equity throughout
the data life cycle, making it a critical 昀椀rst step. Below, we’ve broken out positive and problematic
practices into what we see as three core components of planning.
Team Formation and Governance is about getting the right people and processes in place to foster
equitable data use (see I2D2 and Mecklenburg County Community Support Services). This includes
de昀椀ning both a shared purpose and individual roles, building trust in relationships, ensuring that
power dynamics are illuminated, and establishing transparent decision-making processes. Building
strong governance and sta昀케ng upfront will ultimately support more equitable and participatory
decision-making when important questions arise throughout the data life cycle. For example,
many data sharing efforts wrestle with whether individual-level consent for data sharing and use
is required when legal requirements are unclear. Such challenges can be minimized through the
development of strong, collaboratively generated governance agreements that clearly lay out
processes for decision-making.
Lastly, Project Planning with an equity lens involves learning about the context and history
surrounding data and systemic racism (see Embrace Boston and Connecticut O昀케ce of Early
Childhood). It then requires that we align our research questions and approach to community needs,
the available resources and data, and areas where there is traction for change (see Philadelphia
Monument Lab). As shown in the Joint Statement on Enforcement Efforts Against Discrimination
and Bias in Automated Systems, effective planning can also involve highlighting the risks of data
sharing along with the potential bene昀椀ts. See the Companion Workbook (1.III) for guidance on
facilitating this type of discussion. Thoughtful planning that centers equity from the start paves the
way for more responsible and impactful data use throughout the rest of the data life cycle.
CENTERING RACIAL EQUITY THROUGHOUT THE DATA LIFE CYCLE
Individual and Organizational Readiness refers to the work of individuals and teams within
institutions to operationalize values, ensuring alignment between principles undergirding the work
and operations (see Baltimore’s Promise and the Allied Media Project in the Work in Action section
below). This also involves individuals and the broader team reckoning with their identity and biases,
to develop and re昀椀ne skills needed to engage in equity and engagement work authentically, without
in昀氀icting harm on community members (see Open Data Charter). Readiness work also includes
efforts to ready community members for active roles in data projects (see Black Researchers
Collective).
11 Duty of care is a legal obligation to act with “reasonable” care to prevent foreseeable harm to others. For data sharing and
integration, the duty of care involves providing a “reasonable” amount of protection (e.g., safeguarding data assets) to
constituents, at a minimum, but it is also implicitly owed to the community we aim to serve.
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