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.
Ethics, Bias, and Transparency for People and Machines
This opportunity has closed 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)
This opportunity has closed 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 November 10, 2021