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What Is Needed for AI to Succeed?

Artificial intelligence is affecting many of the challenges facing the nation today—like the pandemic, unemployment, and racial inequities—and bringing new ones to the forefront. AI is also having significant effects on virtually every sector of the economy, but the United States faces several bottlenecks to responsibly unlocking its full potential. These bottlenecks include developing a skilled workforce, ensuring inclusivity, optimizing data use, encouraging public and private research and development, building computing capacity, establishing good technical standards, and fostering public trust and positive attitudes towards AI. Achieving these objectives will require a strong effort and collaboration among the public and private sector.

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Skilled workforce

Advancing and utilizing AI technologies requires a skilled workforce. Currently, there is a considerable AI talent shortage that needs to be addressed. Attracting, training, and retaining AI talent becomes critical in this environment. The U.S. educational system and workforce training programs will play a key role in this matter, as will immigration policies that properly balance various policy goals.

In addition to closing the AI talent gap, policymakers need to think about how to deal with the broad disruptions caused by AI and automation throughout the economy with jobs both created and destroyed. Forecasters anticipate growth in demand for skills and tasks that require creative thinking, analytical skills, and emotional intelligence. However, transitioning workers will not be easy, and policymakers will need to update existing workforce training and educational programs to manage the disruption.

Inclusion

Technology can greatly exacerbate inequality and social disruption if mismanaged, but it also has the potential to improve living standards and create new opportunities for the country. The fruits of growth from technology need to be shared across the board and meaningfully include underrepresented communities and marginalized groups. Therefore, policymakers should make promoting inclusivity a top priority. Further, inclusivity and diversity in the technology sector can help ensure that AI systems and products are designed by a workforce that reflects the broader makeup of society.

Promoting inclusivity in the economy requires a holistic approach. Policymakers should carefully evaluate workforce training programs, the social safety net, and other relevant policies to ensure they are well-suited for the AI-driven economy.

Data

Data plays a critical role in training AI systems. Access to quality data will be a major source of competitive advantage for many private firms and governments. The AI-driven economy will require continued thinking about ways to better collect, store, share, and utilize data, while promoting security, privacy, and consumer protections. Policymakers should thoroughly consider and review policies and initiatives, such as federal data hubs and best practice guidelines, when trying to address the various data challenges AI brings.

Standards and Frameworks

Standards and frameworks can help promote innovation in a responsible manner by providing AI practitioners with a common language and specific ethical and quality standards to abide by. The National Institute of Standards and Technology has been a leader in helping develop standards for AI. NIST’s plan for developing AI technical standards emphasizes that U.S. government agencies should prioritize those that are “inclusive and accessible, open and transparent, consensus-based, globally relevant, and non-discriminatory.” Policymakers should encourage the further development of technical standards.

Research and Development

The United States has historically been a leader in R&D. Public and private investment, as well as public-private partnerships, are important to funding research in AI technologies and applications. R&D can help accelerate innovation and the deployment of AI technologies. A report by the Center for Security and Emerging Technology (CSET) suggested that the amount of research funding has not kept up with the opportunities and challenges presented by modern AI technology.

Venture capital funding for AI start-ups increased from $1.1 billion in 2013 to $9.3 billion in 2018. Despite this growth, according to a Center for a New American Security report, the United States has not kept pace with China, which for the first time ever in 2017 led globally in startup funding.

Computing Power

A key barrier to implementing AI technologies is ensuring public and private entities across the country have enough computing power to harness the potential of AI. Therefore, investments in AI R&D need to be coupled with building out computing capacity. According to research by OpenAI, the computing power needed to run the largest AI models would double every three and a half months. A report by CSET argued that while the United States currently leads the global supply chain in semiconductor chip manufacturing (which is a central component to computing power), meeting the future needs for AI innovation will require sustained funding.

Public Trust and Attitudes

Building public trust and confidence in AI technologies is critical to reaping the benefits of AI and encouraging widespread adoption in a responsible manner. Policymakers and companies need to meaningfully address and allay public concern about AI systems being used for malicious purposes in order to shift attitudes towards supporting the technology. Specifically, addressing concerns about risks around privacy, security, transparency, fairness, civil rights, and civil liberties are key. Policymakers and companies should help empower consumers and promote awareness about how AI systems are being used and their challenges to help build public trust.

Final Thoughts

AI is critical to the future of the United States. A coordinated federal effort that builds on what the government has already done can help alleviate a number of bottlenecks to responsibly unlock its full potential. Through strengthening the workforce, promoting inclusivity, advancing good data practices, investing in R&D, providing technical standards and frameworks, increasing computing power, and building public trust the United States can remain a global leader in AI.

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