Artificial Intelligence Initiatives
There is a tremendous opportunity for data-driven discovery across the NIH mission, including from artificial intelligence and machine learning (AI/ML) technologies. This discovery requires findable, accessible, interoperable, and reusable (FAIR) and AI/ML-ready data. Making data FAIR and AI/ML-ready requires interdisciplinary skills not typically held by biomedical and behavioral researchers. Particularly for biomedical data, AI/ML-readiness should be guided by a concern for human and clinical impact and therefore requires attention to ethical, legal, and social implications of AI/ML, such as biases in datasets, algorithms, and applications; concerns related to privacy and confidentiality; impacts on disadvantaged or marginalized groups and health disparities; and unintended, adverse social consequences of research and development.
To address these challenges, the NIH Office of Data Science Strategy is currently leading three trans-NIH initiatives.
Addressing the Workforce Gap in Data Governance for AI in Biomedicine
Ethics, Bias, and Transparency for People and Machines
Improving the AI-readiness of Existing, IC-supported Data
Two funding opportunities related to these initiatives are currently open for applications:
Apply by May 14: Administrative Supplements for Workforce Development at the Interface of Information Sciences, Artificial Intelligence and Machine Learning (AI/ML), and Biomedical Sciences (NOT-OD-21-079)
- The purpose is to support the development and implementation of curricular or training activities at the interface of information science, AI/ML, and biomedical sciences to develop the competencies and skills needed to make biomedical data FAIR (findable, accessible, interoperable, and reusable) and AI/ML-ready.
- Frequently Asked Questions (FAQs)
Apply by May 26: Administrative Supplements to Support Collaborations to Improve the AI/ML-Readiness of NIH-Supported Data (NOT-OD-21-094)
- This opportunity is intended to support collaborations that bring together expertise in biomedicine, data management, and AI/ML to improve the AI/ML-readiness of data generated from NIH-funded research and shared through repositories, knowledgebases or other data sharing resources.
This page last reviewed on April 23, 2021