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.
To better understand what might drive the way we live, learn, and work in 2050, we’re asking the community to share their expertise and thoughts about how key factors like research and development infrastructure and automation will shape the trajectory of the ecosystem.
Recognizing the power of the national transportation infrastructure expert community and its distributed expertise, ARPA-I took a different route that would instead bring the full collective brainpower to bear around appropriately ambitious ideas.
NIH needs to seriously invest in both the infrastructure and funding to undertake rigorous nutrition clinical trials, so that we can rapidly improve food and make progress on obesity.
Modernizing ClinicalTrials.gov will empower patients, oncologists, and others to better understand what trials are available, where they are available, and their up-to-date eligibility criteria, using standardized search categories to make them more easily discoverable.