The National Artificial Intelligence Research and Development Strategic Plan
The National Artificial Intelligence Research And Development Strategic Plan
The National Artificial Intelligence Research and Development Strategic Plan establishes a set of objectives for federally-funded AI research, both within the government and externally in academia. It identifies key priorities such as long-term AI investments, human-AI collaboration, ethical and legal implications, safety, shared public datasets, standards, and workforce needs. The plan aims to produce new AI knowledge and technologies that benefit society while minimizing negative impacts.
Summary and content fields are drawn from the document text and are pending editorial review.
Who issued it
Who it governs
Federal agencies, academic researchers, and policymakers involved in guiding and funding artificial intelligence research and development.
What the document contains
The document covers seven strategic priorities for federally funded artificial intelligence research and development, focusing on long-term investments, human-AI collaboration, ethical and societal implications, safety and security, shared datasets, standards, and workforce needs. It also outlines recommendations for an implementation framework and a study of the national AI workforce landscape.
The text does not contain binding legal regulations or enforcement mechanisms for commercial AI deployers, nor does it provide specific technical definitions or compliance requirements outside of federal R&D guidance.
- Federal R&D investments should prioritize the next generation of foundational AI research.
- Research must focus on creating effective interactions and collaboration between humans and AI.
- Ethical, legal, and societal implications must be understood and addressed in AI design.
- AI systems must be designed to operate safely, securely, and reliably before widespread use.
- Shared public datasets and evaluative standards are essential for measuring and benchmarking AI progress.
· Strategy 1: Make long-term investments in AI research. · Strategy 2: Develop effective methods for human-AI collaboration. · Strategy 3: Understand and address the ethical, legal, and societal implications of AI. · Strategy 4: Ensure the safety and security of AI systems. · Strategy 5: Develop shared public datasets and environments for AI training and testing. · Strategy 6: Measure and evaluate AI technologies through standards and benchmarks. · Strategy 7: Better understand the national AI R&D workforce needs.
How it scores
Each score is backed by a quote verified against the document text. Editorial review has not yet confirmed them.
Accountability
0/5Clear responsibility for AI outcomes
Executive Summary
screening score · 2026-10-23
Fairness
2/5Non-discrimination and equitable treatment
Research is needed to understand the ethical, legal, and social implications of AI, and to develop methods for designing AI systems that align with ethical, legal, and societal goals.
Strategy 3
screening score · 2026-10-23
Human Dignity
0/5Respect for the inherent worth of persons
Executive Summary
screening score · 2026-10-23
Human Oversight
3/5Humans remain in control of consequential decisions
Rather than replace humans, most AI systems will collaborate with humans to achieve optimal performance.
Strategy 2
screening score · 2026-10-23
Privacy
0/5Protection of personal data and private life
Executive Summary
screening score · 2026-10-23
Transparency
3/5Openness about how AI systems work and are used
Essential to advancements in AI are standards, benchmarks, testbeds, and community engagement that guide and evaluate progress in AI.
Strategy 6
screening score · 2026-10-23
Enforcement
0/5Consequences and compliance mechanisms with real force
Executive Summary
screening score · 2026-10-23
Impact Assessment
3/5Required assessment of an AI system's risks/impacts before or during use
Before AI systems are in widespread use, assurance is needed that the systems will operate safely and securely
Strategy 4
screening score · 2026-10-23