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 improve program outcomes, federal evaluation officers should conduct “unmet desire surveys” to advance federal learning agendas and built agency buy-in.
At least 40% of Medicare beneficiaries do not have a documented AHCD. In the absence of one, medical professionals may perform major and costly interventions unknowingly against a patient’s wishes.
AI has transformative potential in the public health space, but innovation driven primarily by the private sector today may be exacerbating existing disparities by training models.
With targeted policy interventions, we can efficiently and effectively support the U.S. innovation economy through the translation of breakthrough scientific research from the lab to the market.