What is the best way to advance the artificial intelligence (AI) revolution while also addressing the risks surrounding algorithmic technologies? Writing in The Ripon Forum this month, Rep. Jay Obernolte (R-Calif.) argues for a principled approach to AI governance based on “the values of freedom and entrepreneurship,” instead of “government control of technology, and the anti-democratization of knowledge.”

Obernolte, the only member of Congress with a graduate degree in AI, is exactly right. Unfortunately, however, the current AI policy dialogue is heading in the opposite direction as “calls for regulation in the United States and across the globe have reached a fever pitch from both government and academia.”

Panicked rhetoric and extreme proposals are now commonplace in AI policy debates. At a Senate Judiciary Committee hearing last month, one lawmaker suggested that we should begin with the assumption that AI wants to kill us, while other lawmakers and witnesses recited a litany of hypothetical worst-case scenarios. Lawmakers are already floating a new technocratic agency and licensing schemes for AI, among other heavy-handed regulatory proposals.

The Importance of Innovation Culture

We need to reset the debate over AI and work toward common-sense policies that are not rooted in fear of the future. Today’s talk of new command-and-control regimes and bureaucracies is counter-productive because such a regime could undermine the enormous benefits of algorithmic systems and negatively affect America’s global competitiveness in the unfolding computational revolution.

“Part of the brilliance of America’s technology industry over these many years is that it has been allowed to flourish in a largely unregulated environment,” Obernolte argues. “This has given our nation the flexibility to remain agile and on the cutting edge of modern innovation, without the interference of burdensome regulations that could have at many stages shut the industry down for good. It has catalyzed our leadership in the field over countries in Europe, Great Britain, and most of Asia,” he correctly concludes.

Put simply, the United States got its innovation culture right for the internet and digital technology, and the nation must now do the same for AI, machine learning (ML) and robotics. As a recent R Street study outlined, the key will be for America to adopt “Flexible, Pro-Innovation Governance Strategies for Artificial Intelligence.” Silver-bullet solutions do not exist, and innovation-crushing bureaucracies are not the place to start.

The Right Principle for Regulation

To address the risks associated with some AI applications, we need to use measured, context-specific governance solutions. Rep. Obernolte identifies how to strike the right balance when arguing that policymakers must avoid AI mandates “that stifle innovation by focusing on mechanisms instead of on outcomes.” What he means, as I elaborated in a new R Street filing to the Department of Commerce, is that “AI governance should be risk-based and should focus on system outcomes instead of system inputs or design.” A recent report from the Center for Data Innovation summarizes this principle: “Regulate performance, not process.”

This is quickly becoming the key issue in AI policy debates. Many regulatory advocates call for layers of preemptive, precautionary regulations for the underlying data sets, models and computational systems involved in creating new algorithmic products (i.e., the inputs or mechanisms on the process side of algorithmic systems). Their goal is to make algorithmic systems more “explainable” and make sure each part of the code is well understood.  

Unfortunately, as noted in a new Federalist Society essay, “explainability is easier in theory than reality,” and converting this principle into a convoluted regulatory process will mean that algorithmic innovation is essentially treated as guilty until proven innocent. A process-oriented regulatory regime in which all the underlying mechanisms are subjected to endless inspection and micromanagement will create endless innovation veto points, politicization, delays and other uncertainties because it will mostly just be a guessing game based on hypothetical worst-case thinking.

We need the opposite approach that Rep. Obernolte identified, which is focused on algorithmic outcomes. What really matters is that AI and robotic technologies perform as they are supposed to and do so in a generally safe manner. A governance regime focused on outcomes and performance treats algorithmic innovations as innocent until proven guilty and relies on actual evidence of harm and tailored, context-specific solutions to it. This principle is the key to balancing entrepreneurship and safety for AI.  

AI Is Already Being Extensively Regulated by Many Government Agencies

Importantly, regulating AI outcomes or performance is already being done through many existing statutes, agency regulations and court-based mechanisms. Too many people assume that AI and ML tech is developing in a state of anarchy when, in reality, algorithmic systems and applications are already governed by a wide variety of policies that address concrete issues in real-time. For example:

This is real-time algorithmic governance in action. Additional regulatory steps may be needed later to fill gaps in current law, but policymakers should begin by acknowledging that a lot of algorithmic oversight authority exists across the federal government’s 434 current agencies or departments, and that many of those bodies are already actively considering how to address AI and robotics policy. In some cases, agencies might already be regulating some autonomous systems too aggressively, as seems to be the case with the Federal Aviation Administration, which has been very slow to allow unmanned commercial drones to take off.

All this regulatory capacity makes it clear that the United States does not need a new technocratic bureaucracy to cover all-things-AI when so many laws, agencies and regulations already exist. The country never had a Consumer Electronics Agency, a Federal Computer Commission or a Bureau of Internet Control, for example. But it would be silly to say that consumer electronics, computers or the internet operate in a state of complete lawlessness. Instead, a wide variety of laws apply, and many issues are also adjudicated in the courts under common law standards, such as product liability; negligence; design defects law; failure to warn; breach of warranty; property law and contract law; and other torts. This same framework can help address AI risks.

Conclusion: Getting the Balance Right

The United States must get its governance balance right for AI. The stakes are very high, as Rep. Obernolte concludes, noting how “legislators in the [European Union] have moved too aggressively” and are “arbitrarily halting the development of artificial intelligence within their borders and allowing the rest of the world to progress while they lag behind.” In this way, AI policy has geopolitical significance. Obernolte says that EU-style regulation “would be particularly harmful in the United States,” particularly as the country faces threats from China and other nations which are racing to keep up. Our nation must not shoot itself in the foot as this race intensifies.

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