Discussions Regarding Equitable Data Participation and Responsibility at OSTP
The Center for Data Innovation has submitted a filing to the Office of Science and Technology Policy (OSTP), offering recommendations for promoting equitable data practices within federal agencies. The filing, which is available for public review and comment, aligns with the Center's broader mission to promote data-driven policy and innovation.
The Center's recommendations emphasize the importance of cross-sector collaboration among public, private, and academic stakeholders. The OSTP is urged to prioritize closing the "data divide" to improve the effectiveness of data-driven services and decision making.
To achieve this, the Center suggests several key strategies:
- Partnerships: The OSTP should establish sector-specific initiatives involving diverse stakeholders to co-develop national AI standards and datasets. AI Centers of Excellence (regulatory sandboxes) could serve as hubs where government, industry, and academia can test AI tools and share data openly to drive progress while ensuring fairness.
- Data Literacy: Addressing workforce challenges, the OSTP should expand training and education to increase AI and data literacy. This would equip workers and agencies to better understand, develop, and govern AI and data systems, improving equitable adoption and reducing skill disparities contributing to the "data divide".
- Closing the "Data Divide": The OSTP should focus on federal efforts such as regulations to increase statistical data accessibility, promoting data sharing under privacy and confidentiality safeguards, and setting minimum data quality standards. These measures aim to democratize access to reliable, representative datasets, ensuring that underrepresented populations and sectors are included in the data ecosystem.
In addition, the OSTP is encouraged to support partnerships to improve access to high-performance computing for underrepresented groups in the field. The filing does not specify any new recommendations beyond those mentioned above.
The deadline for submitting comments on the filing is yet to be announced. The Center's recommendations stress the importance of the OSTP promoting robust data literacy curriculums in U.S. schools, a recommendation that is also highlighted as the second and fifth points in the filing.
These recommendations reflect the Center for Data Innovation’s emphasis on leveraging partnerships, standards, and capacity-building to promote equitable data practices within federal agencies and beyond. While these recommendations are embedded in broader federal AI strategy documents, they underscore the Center's commitment to fostering a more inclusive and equitable data ecosystem.
[References: 2, 3]
[2] Center for Data Innovation. (2022). Building an Inclusive and Equitable Data Ecosystem: A Plan for Federal AI Strategy. Retrieved from https://www.cdi.ai/report/building-an-inclusive-and-equitable-data-ecosystem-a-plan-for-federal-ai-strategy/
[3] Center for Data Innovation. (2022). Ensuring Equitable Data Use by Federal Agencies: A Response to the OSTP RFI. Retrieved from https://www.cdi.ai/report/ensuring-equitable-data-use-by-federal-agencies-a-response-to-the-ostp-rfi/
- The Center for Data Innovation recommends the Office of Science and Technology Policy (OSTP) to prioritize AI and data literacy education to prepare the workforce for understanding and developing AI and data systems, thereby promoting equitable adoption.
- In addition to expanding training and education, the Center suggests the OSTP should collaborate with industry, government, and academia to establish AI Centers of Excellence, where AI tools can be tested and data shared to drive progress and ensure fairness.
- To democratize access to reliable datasets and bridge the "data divide", the OSTP should focus on federal regulations that increase statistical data accessibility, promote data sharing under privacy and confidentiality safeguards, and set minimum data quality standards.
