AI Classifications for Law and Regulation

This episode discusses the complex landscape of AI classifications and their associated legal regulations. We highlight the need for clear, unified definitions and regulations pertaining to AI to mitigate legal ambiguity.

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About the author
Ted Claypoole (bio)

Transcript

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Welcome to Womble Perspectives, the podcast where we dig into today’s pressing legal issues. Today we’re summarizing an article by Ted Claypoole, lead of our IP Transactions and FinTech Teams. Ted’s article is part of his HeyDataData blog and originally appeared in Business Law Today.

As Ted says in the article, the term “Artificial Intelligence” just isn’t helpful when it comes to public discourse. Reason being, Artificial Intelligence is simply not intelligent. The term encompasses too much, is poorly defined, and as a result, can’t be discussed precisely.

But, it’s still important for policy-makers to understand what they are encouraging or prohibiting. Passing a law to quote unquote restrict artificial intelligence is a dangerous exercise with current definitions.

Different functions of artificial intelligence create different problems for law and society. Generative AI creates not only new text, code, audio or video, but problems with deepfakes, plagiarism and falsehoods presented as convincing facts. AI that predicts whether a prisoner is likely to commit future crimes raises issues of bias, fairness and transparency. AI operating multi-ton vehicles on the road creates physical risks to human bodies. AI that masters the game the chess may not raise any societal issues at all. So why would politicians and courts treat them the same?

They shouldn’t, but if people don’t understand the distinctions between functional types of artificial intelligence, then they won’t be able to make sensible rules. We need to think differently about AI before treating it.

Some of what we think of as AI is nothing more than complex versions of traditional computational algorithms. Standard big-data mining can seem miraculous, but no machine-learning modules are needed to elicit the desired results. And yet, when regulators discuss strapping restrictive rules onto AI, they would include standard algorithms.

Science fiction writer Ted Chiang has defined artificial intelligence as “a poor choice of words in 1954,” preferring instead to call our current technologies “applied statistics.” He also observed that humanized language for computer activities misleads our thinking about amazing, but deeply limited tools, like effective weather predictors and art generators. 

So whether our problem is understandable-but-unfortunate humanization of these models, whether it is imprecise thinking about what types of technology constitutes AI, or whether it is lumping together of disparate functionalities into a single unmanageable term, we are harming the discourse – and our ability to diagnose and treat disfunction – by using the term “artificial intelligence” the way we do now.

If we wish to police AI, our society needs to define and discuss AI precisely.

In the explosion of commentary surrounding generative AI, hand-wringing about singularities devolved into an oft-expressed desire to regulate and otherwise “build guardrails” for AI. Society’s protectors, elected and otherwise, believe that we must stop AI before AI stops us, or at least before our use of AI foments foreseeable harm to populations of innocents.

What we casually call AI right now is a set of computerized and database driven functionalities that should not be considered – and certainly should not be regulated – as a single unit with a single rule. AI consists of too many tools raising too many separate and unrelated societal problems. Instead, if we wish to effectively legislate AI, we should break the definition into functional categories that raise similar issues for the people affected by the technology in that category.

In the article, Ted proposes a modest organizational scheme to assist lawyers, judges, legislators and regulators to 1) grasp the present state of AI and 2) design rules to regulate the functions of machine learning modules. Some of the lines are blurry, and some technical or social problems are shared across classifications, but, as Ted notes, thinking of current AI solutions in legally-significant functional categories will simplify effective rulemaking.

Each of the categories provides a unique set of problems, and Legislators and regulators should be thinking of AI in the following eight functions: Decisioning AI, Personal Identifying AI, Generative AI, Physical Action AI, Differentiating AI, Strategizing AI, Military AI, and automated AI.

The article goes into more detail on each category, and you’ll find a link in the show notes. As mentioned previously, having defined classifications should help law makers better assess how to regulate different AI technologies.

AI exists in extensive forms and functionalities, so attempting to regulate the entire set of technologies would be overreaching and likely ineffective. The above categorizations provide a safer place to start if we wish to regulate a vast and shifting technology. By adopting this thinking, AI management becomes less daunting and more effective.

That’s all for today’s episode. As always, thank you for listening.

 Thank you for listening to Womble Perspectives. If you want to learn more about the topics discussed in this episode, please visit The Show Notes, where you can find links to related resources mentioned today. The Show Notes also have more information about our attorneys who provided today's insights, including ways to reach out to them.

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