Regulation fails – it’s impossible to define AI without negative tradeoffs.

New York City isn’t alone in this struggle. As policymakers around the world have attempted to create guidance and regulation for AI’s use in settings ranging from school admissions and home loan approvals to military weapon targeting systems, they all face the same problem: AI is really challenging to define.

Subtle differences in definition—as well as the overlapping and loaded terminology different actors use to describe similar techniques—can have major impacts on some of the most important problems facing policymakers. Researchers typically refer to techniques that infer patterns from large sets of data as “machine learning,” yet the same concept is often labeled “AI” in policy—conjuring the specter of systems with superhuman capabilities rather than narrow and fallible algorithms. And some technologies commercially marketed as AI are so straightforward that their own engineers would describe them as “classic statistical methods.”

In attempting to better define AI for legislation or regulation, policymakers face two challenging trade-offs: whether to use a technical or human-based vocabulary, and how broad of a scope to use. But despite the difficulty of these tradeoffs, there’s often a way for policymakers to craft an AI definition well-suited to the specific application in question.

The first trade-off pits definitions based on humans against ones based on specific technical traits. Human-based definitions describe AI with analogies to human intelligence. For example, a U.S. Department of Defense strategy defines AI as “the ability of machines to perform tasks that normally require human intelligence.” By contrast, capability-based definitions describe AI through specific technical competencies. One influential definition describes a “machine-based system” that produces “predictions, recommendations, or decisions.”

Human-based definitions naturally accommodate advances in technology. Take the AI research community, which has little need for legal precision: its vague definitions of AI have attracted funding to a broad set of problems and maintained a coherent research community, even as notions of which approaches are most promising have evolved dramatically. And by de-emphasizing specific technical traits, human-based definitions can better focus on the sociotechnical contexts that AI systems operate in. Considering this broader context—rather than just particular technical aspects—is necessary for regulators to understand how AI systems impact people and communities.

By contrast, the specificity of capability-based definitions can better support legal precision, an important consideration for incentivizing innovation and supporting the rule of law. However, these definitions will quickly become outmoded if not carefully targeted to very specific policy problems.

Consider the rapidly advancing field of generative machine learning, which has been used to produce AI-created artwork and artificial but realistic-seeming media known as “deepfakes.” The definition of AI used in a recent EU policy draft explicitly includes systems that generate “content,” in addition to “predictions, recommendations, or decisions.” But the slightly older OECD definition that the legislation was based on mentions only systems that make “predictions, recommendations, or decisions,” arguably excluding content-generation systems. Though lacking precision, human-based definitions can more easily accommodate these kinds of developments in technological capabilities and impacts.

The second trade-off centers on whether AI definitions should be tailored to complex modern systems, or whether they should also include classical algorithms. A key distinction of modern AI systems, such as the deep learning techniques that have driven recent advances, is their complexity. At the extreme, recent language models like OpenAI’s GPT-3 contain billions of parameters and require millions of dollars’ worth of computation to train. Policymakers must consider the unique risks and harms posed by these systems, but the impacts of classical algorithms and statistical techniques should not be exempt from regulatory attention as a result.