NIH to Create Coordinating Center for AI Diversity Efforts
The National Institutes of Health (NIH) plans to fund a coordinating center to increase the participation and representation of researchers and communities currently underrepresented in the development of artificial intelligence/machine learning models and enhance the capabilities of this emerging technology, beginning with electronic health record data.
In its description of the funding opportunity, the NIH notes that the AI/ML field lacks diversity in its researchers and in data, including electronic health record (EHR) data. “These gaps pose a risk of creating and continuing harmful biases in how AI/ML is used, how algorithms are developed and trained, and how findings are interpreted. Critically, these gaps can lead to continued health disparities and inequities for underrepresented communities,” according to the website for the Artificial Intelligence/Machine Learning Consortium to Advance Health Equity and Researcher Diversity (AIM-AHEAD) program.
Underrepresented communities have untapped potential to contribute new expertise, data, recruitment strategies, and cutting-edge science to the AI/ML field. To close the gaps in the field and to better engage underrepresented communities, the NIH has launched the program to increase the participation and representation of the researchers and communities that are currently underrepresented in AI/ML modeling and applications through mutually beneficial partnerships.
The AIM-AHEAD Coordinating Center (A-CC) will build a consortium of institutions and organizations that have a core mission to serve underrepresented or underserved groups (minority populations, low socioeconomic, rural, sexual gender minorities) impacted by health disparities (e.g., Historically Black Colleges and Universities, Tribally Controlled Colleges and Universities, etc.).
The A-CC will focus initially on coordination, assessment, planning, and capacity building to enhance the use of AI and ML in research among the consortium institutions and organizations; and to build and sustain trusted relationships between the consortium and groups impacted by health disparities. The A-CC will be comprised of four main cores:
• Leadership/Administrative Core: Lead the overall A-CC, recruit and coordinate consortium members , project management, partnerships, stakeholder engagement, and outreach to enhance the diversity of researchers in AI/ML related research, with emphasis on health disparities research, and to establish trusted relationships with health disparities groups to enhance the diversity of data used in AI/ML research.
• Data Science Training Core: Assess, develop, and implement data science training curriculum to enhance capacity among diverse population groups, specifically underrepresented or underserved groups impacted by health disparities.
• Data and Research Core: Determine and address research priorities and needs in linking and preparing linking and preparing multiple sources and types of research data to form an inclusive basis for AI/ML use cases that will illuminate strategies and approaches to ameliorate health disparity. This may include facilitating the extraction and transformation of data from EHRs for research use and consideration of social determinants of health as crucial contributors to health.
• Infrastructure Core: Assessment of data, computing, and software infrastructure models, tools, resources, data science policies, and AI/ML computing models that will facilitate AI/ML and health disparities research; and establishment of pilot data and analysis environments to accelerate overall A-CC aims.
The current budget for this award period is planned for up to $100 million over a two-year period including the operations and support for the A-CC as well as any proposed activities, projects, infrastructure, or investments for members of the consortium, once established.
Proposals are due by Aug. 3, 2021, with the earliest start date of Sept. 8, 2021.
