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.
At a time when universities are already facing intense pressure to re-envision their role in the S&T ecosystem, we encourage NSF to ensure that the ambitious research acceleration remains compatible with their expertise.
FAS CEO Daniel Correa recently spoke with Adam Marblestone and Sam Rodriques, former FAS fellows who developed the idea for FROs and advocated for their use in a 2020 policy memo.
When the U.S. government funds the establishment of a platform for testing hundreds of behavioral interventions on a large diverse population, we will start to better understand the interventions that will have an efficient and lasting impact on health behavior.
Integrating AI tools into healthcare has an immense amount of potential to improve patient outcomes, streamline clinical workflows, and reduce errors and bias.