Reps. Will Hurd (R-TX) and Robin Kelly (D-IL) introduced a bipartisan House resolution today calling for the United States to create a national strategy for artificial intelligence (AI). Many nations, including Canada, China, France, Russia, South Korea, and the United Kingdom, have developed formal national strategies to support the development and adoption of AI. While the Trump administration has made important progress in putting in place policies to support AI, it is important for Congress to build on these efforts.

Their resolution endorses five guiding principles for a national AI strategy: maintaining global leadership in AI, preparing the workforce, strengthening national security, fostering mechanisms to address bias, fairness, and privacy issues, and advancing AI research and development. Given the fierce competition in AI and the significant economic and national security implications of ceding leadership, it is high time Congress create and fund a cohesive AI strategy. 

First, the resolution calls for the United States to take a global leadership role in AI. It advocates for the United States to strengthen its participation in international forums that set AI principles, norms, and standards, a step the United States has already rightly moved towards by joining the G7 partnership on artificial intelligence. This will be critical to assuring that global rules and norms are more aligned with the U.S. approach of enabling business to drive responsible innovation.

Second, the resolution calls for the United States to increase funding for technology education programs to equip students and workers with the skills they need to participate in the AI-driven economy. AI skills are in short supply for industry and government which is why the resolution calls for government agencies to review their hiring practices to ensure they do not disqualify individuals with AI skills who do not have degrees, building on President Trump’s executive order regarding overall federal hiring. Accepting alternative certifications when filling federal government jobs in AI will demonstrate the feasibility of using these credentials to the private sector and work towards separating education from credentialing.  

Third, the resolution calls for academia, the private sector, and agencies of the federal government to work together in developing best practices for testing, evaluating, validating, and verifying AI systems vital to the military. It encourages the United States to leverage its alliances to both prevent China and Russia misusing AI systems and establish procedures to reduce the likelihood of the United States, or its allies, using their own AI systems in unintended ways. This will be important in ensuring public trust in the ability of the U.S. military to ethically use AI systems and help the United States maintain a strategic military advantage over China.

Fourth, the resolution calls for the federal government to support technical and non-technical research in how to ensure AI systems are ethical, reduce bias, promote fairness, and protect privacy. It encourages the government to make non-sensitive government datasets available to help reduce bias in the data researchers use to train AI systems and advocates for the government to review existing laws and regulation to identify if, and how, it can address any gaps in AI regulation. 

Finally, the resolution calls for the government to increase funding for AI research to ensure the U.S. AI innovation ecosystem continues to lead the world. This will be important in fostering research that leads to advances in AI, such as how to better train AI models on smaller data sets or how to make more efficient use of computing resources. 

Developing a national strategy for AI is important to bolstering U.S. competitiveness, strengthening national security, and maximizing the societal benefits that the country could derive from AI, so Congress should move to pass this resolution and then take the necessary steps to fulfill this vision. 

By Hodan Omaar, Policy Analyst at the Center for Data Innovation