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.
The real opportunity of AI lies not just in the tools, but in an educator workforce prepared to wield them. When done right, this investment in human infrastructure ensures AI accelerates learning outcomes for all students, closing the “digital design divide.”
Good information sources, like collections, must be available and maintained if companies are going to successfully implement the vision of AI for science expressed by their marketing and executives.
Nestled in the cuts and investments of interest to the S&T community is a more complex story of how the administration is approaching the practice of science diplomacy.
By structuring licensing-and-talent deals that replicate mergers while avoiding antitrust scrutiny, dominant technology firms are reshaping AI labor markets, venture financing, and the future of U.S. innovation.