Artificial Intelligence: Background, Selected Issues, and Policy Considerations

The field of artificial intelligence (AI)—a term first used in the 1950s—has gone through multiple waves of advancement over the subsequent decades. Today, AI can broadly be thought of as computerized systems that work and react in ways commonly thought to require intelligence, such as the ability to learn, solve problems, and achieve goals under uncertain and varying conditions. The field encompasses a range of methodologies and application areas, including machine learning (ML), natural language processing, and robotics.
Federal activity addressing AI accelerated during the 115th and 116th Congresses. President Donald Trump issued two executive orders, establishing the American AI Initiative (E.O. 13859) and promoting the use of trustworthy AI in the federal government (E.O. 13960). Federal committees, working groups, and other entities have been formed to coordinate agency activities, help set priorities, and produce national strategic plans and reports, including an updated National AI Research and Development Strategic Plan and a Plan for Federal Engagement in Developing Technical Standards and Related Tools in AI. In Congress, committees held numerous hearings, and Members introduced a wide variety of legislation to address federal AI investments and their coordination; AI-related issues such as algorithmic bias and workforce impacts; and AI technologies such as facial recognition and deepfakes. At least four laws enacted in the 116th Congress focused on AI or included AI-focused provisions.
AI holds potential benefits and opportunities, but also challenges and pitfalls. For example, AI technologies can accelerate and provide insights into data processing; augment human decisionmaking; optimize performance for complex tasks and systems; and improve safety for people in dangerous occupations. On the other hand, AI systems may perpetuate or amplify bias, may not yet be fully able to explain their decisionmaking, and often depend on vast datasets that are not widely accessible to facilitate research and development (R&D). Further, stakeholders have questioned the adequacy of human capital in both the public and private sectors to develop and work with AI, as well as the adequacy of current laws and regulations for dealing with societal and ethical issues that may arise. Together, such challenges can lead to an inability to fully assess and understand the operations and outputs of AI systems—sometimes referred to as the “black box” problem.
Because of these questions and concerns, some stakeholders have advocated for slowing the pace of AI development and use until more research, policymaking, and preparation can occur. Others have countered that AI will make lives safer, healthier, and more productive, so the federal government should not attempt to slow it, but rather should give broad support to AI technologies and increase federal AI funding.
In response to this debate, Congress has begun discussing issues such as the trustworthiness, potential bias, and ethical uses of AI; possible disruptive impacts to the U.S. workforce; the adequacy of U.S. expertise and training in AI; domestic and international efforts to set technological standards and testing benchmarks; and the level of U.S. federal investments in AI research and development and how that impacts U.S. international competitiveness. Congress is likely to continue grappling with these issues and working to craft policies that protect American citizens while maximizing U.S. innovation and leadership.    Purchase on Amazon