March 2023

I have a new R Street Institute policy study out this week doing a deep dive into the question: “Can We Predict the Jobs and Skills Needed for the AI Era?” There’s lots of hand-wringing going on today about AI and the future of employment, but that’s really nothing new. In fact, in light of past automation panics, we might want to step back and ask: Why isn’t everyone already unemployed due to technological innovation?

To get my answers, please read the paper! In the meantime, here’s the executive summary:

To better plan for the economy of the future, many academics and policymakers regularly attempt to forecast the jobs and worker skills that will be needed going forward. Driving these efforts are fears about how technological automation might disrupt workers, skills, professions, firms and entire industrial sectors. The continued growth of artificial intelligence (AI), robotics and other computational technologies exacerbate these anxieties.

Yet the limits of both our collective knowledge and our individual imaginations constrain well-intentioned efforts to plan for the workforce of the future. Past attempts to assist workers or industries have often failed for various reasons. However, dystopian predictions about mass technological unemployment persist, as do retraining or reskilling programs that typically fail to produce much of value for workers or society. As public efforts to assist or train workers move from general to more specific, the potential for policy missteps grows greater. While transitional-support mechanisms can help alleviate some of the pain associated with fast-moving technological disruption, the most important thing policymakers can do is clear away barriers to economic dynamism and new opportunities for workers.

I do discuss some things that government can do to address automation fears at the end of the paper, but it’s important that policymakers first understand all the mistakes we’ve made with past retraining and reskilling efforts. The easiest thing to do to help in the short-term is clear away barriers to labor mobility and economic dynamism, I argue. Again, read the study for details.

For more info on other AI policy developments, check out my running list of research on AI, ML robotics policy.

This week, the U.S. Chamber of Commerce Commission on Artificial Intelligence Competitiveness, Inclusion, and Innovation (AI Commission) released a major report on the policy considerations surrounding AI, machine learning (ML) and algorithmic systems. The 120-page report concluded that “AI technology offers great hope for increasing economic opportunity, boosting incomes, speeding life science research at reduced costs, and simplifying the lives of consumers.” It was my honor to serve as one of the commissioners on the AI Commission and contribute to the report.

Over at the R Street Institute blog, I offer a quick summary of the major findings and recommendations from the report and argue that, along with the National Institute of Standards and Technology (NIST)’s recently released AI Risk Management Framework, the AI Commission report offers, “a constructive, consensus-driven framework for algorithmic governance rooted in flexibility, collaboration and iterative policymaking. This represents the uniquely American approach to AI policy that avoids the more heavy-handed regulatory approaches seen in other countries and it can help the United States again be a global leader in an important new technological field,” I conclude. Check out the blog post and the full AI Commission report if you are following debates of algorithmic policy issues. There’s lot of important material in there.

For more info on AI policy developments, check out my running list of research on AI, ML robotics policy.