Artificial Intelligence & Robotics

Why Isn’t Everyone Already Unemployed Due to Automation?

by on March 11, 2023 · 0 comments

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.

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US Chamber AI Commission Launches

by on March 11, 2023 · 0 comments

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.

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7 AI Policy Issues to Watch in 2023 and Beyond

by on February 10, 2023 · 0 comments

In my latest R Street Institute blog post, “Mapping the AI Policy Landscape Circa 2023: Seven Major Fault Lines,” I discuss the big issues confronting artificial intelligence and machine learning in the coming year and beyond. I note that the AI regulatory proposals are multiplying fast and coming in two general varieties: broad-based and targeted. Broad-based algorithmic regulation would address the use of these technologies in a holistic fashion across many sectors and concerns. By contrast, targeted algorithmic regulation looks to address specific AI applications or concerns. In the short-term, it is more likely that targeted or “sectoral” regulatory proposals have a chance of being implemented.

I go on to identify seven major issues of concern that will drive these policy proposals. They include:

1) Privacy and Data Collection

2) Bias and Discrimination

3) Free Speech and Disinformation

4) Kids’ Safety

5) Physical Safety and Cybersecurity

6) Industrial Policy and Workforce Issues

7) National Security and Law Enforcement Issues

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AI Policy Research: My Year in Review

by on December 26, 2022 · 0 comments

I spent much of 2022 writing about the growing policy debate over artificial intelligence, machine learning, robotics, and the Computational Revolution more generally. Here are some of the major highlights of my work on this front.

All these essays + dozens more can be found on my: “Running List of My Research on AI, ML & Robotics Policy.” I have several lengthy studies and many shorter essays coming in the first half of 2023.

Finally, here is a Federalist Society podcast discussion about AI policy hosted by Jennifer Huddleston in which Hodan Omaar of ITIF and I offer a big picture overview of where things are headed next.

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Tech Regulation Will Increasingly Be Driven Through the Prism of “Algorithmic Fairness”

by on November 6, 2022 · 0 comments

We are entering a new era for technology policy in which many pundits and policymakers will use “algorithmic fairness” as a universal Get Out of Jail Free card when they push for new regulations on digital speech and innovation. Proposals to regulate things like “online safety,” “hate speech,” “disinformation,” and “bias” among other things often raise thorny definitional questions because of their highly subjective nature. In the United States, efforts by government to control these things will often trigger judicial scrutiny, too, because restraints on speech violate the First Amendment. Proponents of prior restraint or even ex post punishments understand this reality and want to get around it. Thus, in an effort to avoid constitutional scrutiny and lengthy court battles, they are engaged in a rebranding effort and seeking to push their regulatory agendas through a techno-panicky prism of “algorithmic fairness” or “algorithmic justice.”

Hey, who could possibly be against FAIRNESS and JUSTICE? Of course, the devil is always in the details as Neil Chilson and I discuss in our new paper for the The Federalist Society and Regulatory Transparency Project on, “The Coming Onslaught of ‘Algorithmic Fairness’ Regulations.” We document how federal and state policymakers from both parties are currently considering a variety of new mandates for artificial intelligence (AI), machine learning, and automated systems that, if imposed, “would thunder through our economy with one of the most significant expansions of economic and social regulation – and the power of the administrative state – in recent history.” Continue reading →

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We Need to Get All the Smart People in a Room & Have a Conversation

by on October 16, 2022 · 0 comments

For my latest Discourse column (“We Really Need To ‘Have a Conversation’ About AI … or Do We?”), I discuss two commonly heard lines in tech policy circles.

1) “We need to have conversation about the future of AI and the risks that it poses.”

2) “We should get a bunch of smart people in a room and figure this out.”

I note that, if you’ve read enough essays, books or social media posts about artificial intelligence (AI) and robotics—among other emerging technologies—then chances are you’ve stumbled on variants of these two arguments many times over. They are almost impossible to disagree with in theory, but when you start to investigate what they actually mean in practice, they are revealed to be largely meaningless rhetorical flourishes which threaten to hold up meaningful progress on the AI front. I continue on to argue in my essay that:

I’m not at all opposed to people having serious discussions about the potential risks associated with AI, algorithms, robotics or smart machines. But I do have an issue with: (a) the astonishing degree of ambiguity at work in the world of AI punditry regarding the nature of these “conversations,” and, (b) the fact that people who are making such statements apparently have not spent much time investigating the remarkable number of very serious conversations that have already taken place, or which are ongoing, about AI issues.

In fact, it very well could be the case that we have too many conversations going on currently about AI issues and that the bigger problem is instead one of better coordinating the important lessons and best practices that we have already learned from those conversations.

I then unpack each of those lines and explain what is wrong with them in more detail. Continue reading →

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No Goldilocks Formula for Content Moderation in Social Media or the Metaverse, But Algorithms Still Help

by on September 13, 2022 · 0 comments

[Cross-posted from Medium.]

In an age of hyper-partisanship, one issue unites the warring tribes of American politics like no other: hatred of “Big Tech.” You know, those evil bastards who gave us instantaneous access to a universe of information at little to no cost. Those treacherous villains! People are quick to forget the benefits of moving from a world of Information Poverty to one of Information Abundance, preferring to take for granted all they’ve been given and then find new things to complain about.

But what mostly unites people against large technology platforms is the feeling that they are just too big or too influential relative to other institutions, including government. I get some of that concern, even if I strongly disagree with many of their proposed solutions, such as the highly dangerous sledgehammer of antitrust breakups or sweeping speech controls. Breaking up large tech companies would not only compromise the many benefits they provide us with, but it would undermine America’s global standing as a leader in information and computational technology. We don’t want that. And speech codes or meddlesome algorithmic regulations are on a collision course with the First Amendment and will just result in endless litigation in the courts.

There’s a better path forward. As President Ronald Reagan rightly said in 1987 when vetoing a bill to reestablish the Fairness Doctrine, “History has shown that the dangers of an overly timid or biased press cannot be averted through bureaucratic regulation, but only through the freedom and competition that the First Amendment sought to guarantee.” In other words, as I wrote in a previous essay about “The Classical Liberal Approach to Digital Media Free Speech Issues,” more innovation and competition are always superior to more regulation when it comes to encouraging speech and speech opportunities.

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AI Eats the World: Preparing for the Computational Revolution and the Policy Debates Ahead

by on September 12, 2022 · 0 comments

[Cross-posted from Medium.]

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AI Governance “on the Ground” vs “on the Books”

by on August 24, 2022 · 0 comments

[Cross-posted from Medium]

There are two general types of technological governance that can be used to address challenges associated with artificial intelligence (AI) and computational sciences more generally. We can think of these as “on the ground” (bottom-up, informal “soft law”) governance mechanisms versus “on the books” (top-down, formal “hard law”) governance mechanisms.

Unfortunately, heated debates about the latter type of governance often divert attention from the many ways in which the former can (or already does) help us address many of the challenges associated with emerging technologies like AI, machine learning, and robotics. It is important that we think harder about how to optimize these decentralized soft law governance mechanisms today, especially as traditional hard law methods are increasingly strained by the relentless pace of technological change and ongoing dysfunctionalism in the legislative and regulatory arenas.

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Why the Future of AI Will Not Be Invented in Europe

by on August 1, 2022 · 0 comments

For my latest column in The Hill, I explored the European Union’s (EU) endlessly expanding push to regulate all facets of the modern data economy. That now includes a new effort to regulate artificial intelligence (AI) using the same sort of top-down, heavy-handed, bureaucratic compliance regime that has stifled digital innovation on the continent over the past quarter century.

The European Commission (EC) is advancing a new Artificial Intelligence Act, which proposes banning some AI technologies while classifying many others under a heavily controlled “high-risk” category. A new bureaucracy, the European Artificial Intelligence Board, will be tasked with enforcing a wide variety of new rules, including “prior conformity assessments,” which are like permission slips for algorithmic innovators. Steep fines are also part of the plan. There’s a lengthy list of covered sectors and technologies, with many others that could be added in coming years. It’s no wonder, then, that the measure has been labelled the measure “the mother of all AI laws” and analysts have argued it will further burden innovation and investment in Europe.

As I noted in my new column, the consensus about Europe’s future on the emerging technology front is dismal to put it mildly. The International Economy journal recently asked 11 experts from Europe and the U.S. where the EU currently stood in global tech competition. Responses were nearly unanimous and bluntly summarized by the symposium’s title: “The Biggest Loser.” Respondents said Europe is “lagging behind in the global tech race,” and “unlikely to become a global hub of innovation.” “The future will not be invented in Europe,” another analyst bluntly concluded. Continue reading →

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