The White House has released an updated version of the National Artificial Intelligence Research and Development Strategic Plan for 2023, which outlines the federal government’s priorities and goals for advancing AI innovation and ensuring its responsible use. The plan, which follows previous versions issued in 2016 and 2019, reaffirms eight strategies and adds a ninth one to emphasize the importance of international collaboration in AI research.
Long-term investments in responsible AI research
The first strategy of the plan is to make long-term investments in responsible AI research, which aims to drive innovation that serves the public good and enables the US to remain a world leader in AI. This includes advancing foundational AI capabilities such as perception, representation, learning, and reasoning, as well as focused efforts to make AI easier to use and more reliable and to measure and manage risks associated with generative AI.
Effective methods for human-AI collaboration
The second strategy is to develop effective methods for human-AI collaboration, which seeks to increase understanding of how to create AI systems that effectively complement and augment human capabilities. This involves research on the attributes and requirements of successful human-AI teams, methods to measure the efficiency, effectiveness, and performance of AI-teaming applications, and mitigating the risk of human misuse of AI-enabled applications that lead to harmful outcomes.
Ethical, legal, and societal implications of AI
The third strategy is to understand and address the ethical, legal, and societal implications of AI, which aims to develop approaches to understand and mitigate the ethical, legal, and social risks posed by AI to ensure that AI systems reflect the nation’s values and promote equity. This includes interdisciplinary research to protect and support values through technical processes and design, as well as to advance areas such as AI explainability and privacy-preserving design and analysis.
Ensuring the safety and security of AI systems
The fourth strategy is to ensure the safety and security of AI systems, which focuses on developing methods and standards to ensure that AI systems are robust, reliable, and resilient to adversarial attacks and unintended errors. This includes research on verification, validation, testing, and evaluation of AI systems, as well as on AI assurance, accountability, and governance.
Shared public data and environments for AI training and testing
The fifth strategy is to create shared public data and environments for AI training and testing, which aims to provide high-quality and diverse data and computational resources to support AI R&D across all sectors. This includes research on data curation, annotation, and sharing, as well as on synthetic data generation and simulation environments.
Measuring and evaluating AI technologies through standards and benchmarks
The sixth strategy is to measure and evaluate AI technologies through standards and benchmarks, which seeks to establish common metrics and methods to assess the performance, quality, and impact of AI systems. This includes research on developing and maintaining AI standards and benchmarks, as well as on measuring the economic and social outcomes of AI applications.
Better understanding the national AI R&D workforce needs
The seventh strategy is to better understand the national AI R&D workforce needs, which aims to assess the current and future demand and supply of AI talent and skills across all sectors and regions. This includes research on identifying and addressing the gaps and barriers in the AI education and training pipeline, as well as on enhancing the diversity, equity, and inclusion of the AI workforce.
Expanding public-private partnerships to accelerate advances in AI
The eighth strategy is to expand public-private partnerships to accelerate advances in AI, which focuses on fostering collaboration and coordination among federal agencies, industry, academia, non-governmental organizations, and international partners to leverage their complementary strengths and resources. This includes research on developing and implementing effective models and mechanisms for AI partnerships, as well as on aligning incentives and addressing challenges for AI collaboration.
Developing a coordinated approach to international cooperation on AI
The ninth and new strategy is to develop a coordinated approach to international cooperation on AI, which emphasizes the need to engage with allies and partners to advance shared values and interests in AI. This includes research on identifying and pursuing opportunities and priorities for international AI collaboration, as well as on addressing the challenges and risks of global AI competition and governance.
