Toward Better AI Legislation

“People deserve to know whether or not the videos, photos, and content they see and read online is real or original,” said Senator Schatz. “Our bill is simple – if any digital content is made by artificial intelligence, it should be labeled so that people are aware and aren’t fooled or scammed.”

Senator Schatz is doing a great job and this is clearly a sincere effort for an important issue. However, ‘‘AI Labeling Act of 2026’’ is just one example of how poorly the government is equipped to regulate genAI. We must carefully analyze legislation to anticipate serious side effects potentially making things worse.

Will this work well enough? Probably at least for a while, sure. But I’m arguing (a) we should be keenly aware of limitations this fundamental; (b) acknowledging its weaknesses, evaluate how it works and reevaluate it regularly; (c) instead of deferring to administrative regulations left undefined the law should set up a committee representing diverse perspectives to outline regulations and work with government staff to help craft the details; (d) expect that bad actors and crafty lawyers will quickly notice all the loopholes and gray areas to exploit aggressively in order to circumvent responsibility; (e) consider better approaches rather than stick to such a flawed initial conception.

In closing I will offer some different ideas that I think are at least steps in the right direction.

Briefly, the major problems include:

• The definition of “AI” (15 U.S.C. § 9401(3)) is overly broad (see below) encompassing all kinds of software we wouldn’t consider “AI”. Saying “I know it when I see it” is fine for subjective judgments but applied to categories of software (100% digital) it’s absurd as a legal criterion.
• Model inference is just a (very big) table driven algorithm implemented in code like any other component. All software models information (the concept behind object oriented programming) and produces output that is inferred from those models (often simple but it can be as complex as we make it, however complexity is not a condition for the law anyway).
• Ordinary users do not understand genAI at all and have no way of knowing if tools they are using depend on genAI internally or not. (Labeling of software isn’t included. DMCA forbids reverse analysis to find out if the facts are not accurately disclosed by the maker)
• There are no exceptions for minor uses that are insignificant (e.g. minor photo touchup).
• Reposting content a user believes to be genuine but isn’t would be an unfair violation.
• Expecting civil servants to craft regulations based on a weak definition of “AI” and given the complexities of software, unless they have considerable genAI expertise is almost certain to fail. Virtually all genAI experts are in industry or academia and both already biased and also unlikely to commit to advising the government.
• Content providers need clear guidelines in order to comply with the law, but have no industry consensus definition of “AI”, are generally unaware of the regulatory definition mentioned (with many faults), and implementing identification and protection at scale is infeasible.
• Predictably the $1T scale platforms have technical and legal resources to effectively skirt the law or respond effectively if charged with violation, but smaller competitors will be exposed to arbitrary enforcement risk.

Regarding the definition of “AI” that this legislation is based on (15 U.S.C. § 9401(3)), it says:

• The term “artificial intelligence” means a machine-based system that can, for a given set of human-defined objectives, make predictions, recommendations or decisions influencing real or virtual environments. Artificial intelligence systems use machine and human-based inputs to-
• (A) perceive real and virtual environments;
• (B) abstract such perceptions into models through analysis in an automated manner; and
• (C) use model inference to formulate options for information or action.

There being no carve outs for scale, complexity, parameter size, compute complexity, this would apply to a broad set of software categories that must not be the intention and would be totally infeasible to apply much less enforce. Also over-labeling dulls the value of the label itself (consider California’s Proposition 65 toxic exposure right-to-know law).

• An easy example of this being overly broad (it applies to all machine learning, of any scale) is web algorithms to maximize engagement: human inputs (posts and choices of content); virtual environment (social graph, advertising demand, other web content, etc); build customized models of what each user engages best with; infers what content to push next to extend platform interaction.
• Digital games with 3D world models (that use similar NVIDIA hardware) also have human input, a virtual environment, character behavior as well as physics models, and infer game action or scene visuals.
• Small models fit, too; consider grammar checkers: human inputs (text entry); virtual environment (document context); correct grammar model for inference; action options (suggested fixes). Do we seriously want to require labeling documents written with copy editing help from genAI?

It’s easy to find flaws, but here are some alternative approaches I think deserve consideration:

• Instead of criminalizing mandatory labeling it would be much more effective to hold hosters responsible for enforcement. (This may be politically infeasible due to lobbying by the $1T scale platforms.
• We should punish deceptive behavior, not the technology used. Precedents like gun laws and fraud penalize behavior not technology usage: there is a track record and that approach works quite well.

By no means do I suggest having The Answer but I think there’s a lot to worry about, it calls into question how much expertise is applied in crafting these laws, and we need a clear reckoning of the limitations and risks of passing laws — especially about leading edge technologies.