Response to federal RFI on equitable data engagement & accountability
In September 2022, AISP responded to the White House Office of Science and Technology Policy request for information (RFI) on equitable data engagement and accountability. For more information, see the White House blog here and the formal request for information here. Read AISP’s full response below.
Actionable Intelligence for Social Policy (AISP), based at the University of Pennsylvania, works with state and local governments to connect agency partners so data can be shared securely and used collaboratively to improve outcomes. The AISP Network currently includes 23 states, 9 counties, and 4 large cities that are actively sharing and linking cross-agency data, and we convene regularly to share learnings and best practices. For more than a decade, we have supported and observed significant growth, impact, and innovation in this field, including a more explicit emphasis on equity in data use — and more requests from governments seeking to engage community members and partners in their data sharing efforts.
In response to this growing demand, AISP has developed new tools, training, and technical assistance. Since 2019 we have led a diverse workgroup of civic data partners to co-create strategies and identify best practices to center racial equity and community voice in data integration efforts. This group collaboratively developed A Toolkit for Centering Racial Equity Throughout Data Integration (May 2020). In the Toolkit, we put forward a shared vision for a new kind of data infrastructure — one that seeks to share power and knowledge with those who need systems change the most — and offer strategies to begin the work as well as concrete examples of promising and problematic practice.
Now, we are facilitating our inaugural Equity in Practice Learning Community (EiPLC), which is our next step toward supporting sites in planning and implementing promising practices in centering racial equity in data access and use. The EiPLC offers training, technical support, and peer learning opportunities to state and local data integration efforts as they work to share and build power with community through more equitable data access and use. The six participating sites — Baltimore, MD; Broward County, FL; Charlotte-Mecklenburg, NC; Connecticut; King County, WA; and Oregon — are building, testing, and adapting new models for incorporating community voice in key decisions about cross-sector data use at the state or local level. More information is available on our website: https://aisp.upenn.edu/eiplc/
Through the development of the Toolkit and our Learning Community, we have affirmed that community participation should be understood as an on-going and iterative process that supports trust-building with impacted people and populations. Importantly, we have seen that meaningful participation requires that collaborators are resourced to do the work. Agency and partner staff need dedicated time and training, and compensation for community members should be established early on in project budgets.
We have also found that this work can feel overwhelming in it’s depth and breadth, so it is helpful to orient this work around the data life cycle to determine where process changes can be made. We encourage the federal government to do the same, considering how collaboration and equity can be encouraged at each stage: planning, data collection, data access, use of algorithms and statistical tools, data analysis, and reporting and dissemination.
Below we offer state and local examples of promising, equity-oriented engagement practices at each stage of the data life cycle for your consideration:
- Planning: Utilize Participatory Action Research models to center the expertise of people with lived experience on a given topic and offer training and sustained engagement to this group from the project outset (See the Hartford Opportunity Youth Collaborative)
- Data collection: Work directly with clients (people in the data) and staff (people who collect the data) to develop equity-oriented data collection standards. The process and the outcomes can address concerns, gain trust and buy-in, and better align values across groups. (See Allegheny County, PA DHS in A Toolkit for Centering Racial Equity Throughout Data Integration [Toolkit], p. 54)
- Data access: Host easily accessible and high-quality information about what data are collected, stored, and maintained within the data system. This can help close knowledge gaps between the public, agency analysts, and researchers. For example, clear process around request forms and approvals provide context for how agencies, residents, and providers can access data based on their user type and tools like data use dictionaries that provides context around what data are and are not available, and why (See Kentucky Center for Statistics in Toolkit, p. 24)
- Use of algorithms/statistical tools: Involve community members in the conversations about new tools or systems by working with activists and data advocates to develop tools with accessible language and formatting for engagement with a broad audience. Materials should clearly communicate risks and benefits of algorithms and statistical tools (See Automating.NYC website in Toolkit, p. 27)
- Data analysis: Disaggregate data to analyze intersectional experiences by race, class, gender, and other identities. Consider how consent policies influence who is represented in a data set for analysis (see King County DCHS Data Insight Series)
- Reporting & dissemination: Report data in an actionable — and findable — format to improve the lives of those represented in the data, and provide public access to aggregate data to help drive toward change. Include stories as a complement to quantitative findings in order to better contextualize the lived experiences behind the numbers (See Charlotte-Mecklenburg Housing & Homelessness Data Dashboard)
The ethical sharing and use of data is achieved when strong and transparent governance is at the foundation of all collaboration. Most efforts are largely funded with taxpayer dollars, so clear communication among partners about what data are being shared and for what purpose is essential to accountability. Demonstrating and communicating the value of integrated data to residents, agencies, and collaborators also builds social license. Policies, protocols, and documentation of data use — as well as any specific projects the effort is engaged in — should be readily available to the public in understandable and accessible formats.
While some of these practices highlighted above may be more feasible at the local level, many practices (e.g. creating metadata standards and creating accessible modes of communicating and sharing data) apply equally in the federal context and in collaborations across levels of government. In addition, the federal government has a key role to play in terms of developing standards and resourcing state and local grantees to test and refine these practices.
We’re grateful for the opportunity to contribute our thoughts to the Office of Science and Technology Policy and the Office of Management and Budget, whose emphasis on equitable engagement is encouraging and urgently needed, and we appreciate the broad set of voices being brought to the conversation through this RFI. We are excited for the work ahead.
