A National AI for Good Initiative
Summary
Artificial intelligence (AI) and machine learning (ML) models can solve well-specified problems, like automatically diagnosing disease or grading student essays, at scale. But applications of AI and ML for major social and scientific problems are often constrained by a lack of high-quality, publicly available data—the foundation on which AI and ML algorithms are built.
The Biden-Harris Administration should launch a multi-agency initiative to coordinate the academic, industry, and government research community to support the identification and development of datasets for applications of AI and ML in domain-specific, societally valuable contexts. The initiative would include activities like generating ideas for high-impact datasets, linking siloed data into larger and more useful datasets, making existing datasets easier to access, funding the creation of real-world testbeds for societally valuable AI and ML applications, and supporting public-private partnerships related to all of the above.
By better harnessing the power of data, we can build a learning healthcare system where outcomes drive continuous improvement and where healthcare value leads the way.
In this unprecedented inflection point (and time of difficult disruption) for higher education, science funding, and agency structure, we have an opportunity to move beyond incremental changes and advocate for bold, new ideas that envision a future of the scientific research enterprise that looks very different from the current system.
Assigning persistent digital identifiers (Digital Object Identifiers, or DOIs) and using ORCIDs (Open Researcher and Contributor IDs) for key personnel to track outputs for research grants will improve the accountability and transparency of federal investments in research and reduce reporting burden.
Research funding agencies should apply the content of grant applications to AI tools to predict the future of scientific and technological breakthroughs, enhance peer review, and encourage better research investment decisions by both the public and the private sector.