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San Diego Law Review

Library of Congress Authority File

http://id.loc.gov/authorities/names/n79122466.html

Document Type

Article

Abstract

Generative artificial intelligence (AI) offers tremendous benefits to society. However, these benefits must be carefully weighed against the societal damage AI can also cause. Dangers posed by inaccurate training sets have been raised by many authors. These include racial discrimination, sexual bias, and other pernicious forms of misinformation. One remedy to such problems is to ensure that training sets used to teach AI models are correct and that the data upon which they rely are accurate. An assumption behind this correction is that data inaccuracies are inadvertent mistakes. However, a darker possibility exists: the deliberate seeding of training sets with inaccurate information for the purpose of skewing the output of AI models toward misinformation. As United States Supreme Court Justice Oliver Wendell Holmes, Jr., suggested, laws are not written for the “good man,” because good people will tend to obey moral and legal principles in manners consistent with a well-functioning society even in the absence of formal laws. Rather, Justice Holmes proposed, that laws should be written with the “bad man” in mind, because bad people will push the limits of acceptable behavior, engaging in cheating, dishonesty, crime, and other societally- damaging practices, unless constrained by carefully-designed laws and their accompanying penalties.

This Article raises the spectre of the deliberate sabotage of training sets used to train AI models, with the purpose of perverting the outputs of such models. Examples include fostering revisionist histories, unjustly harming or rehabilitating the reputations of people, companies, or institutions, or even promoting as true ideas that are not. Strategic and clever efforts to introduce ideas into training sets that later manifest themselves as facts could aid and abet fraud, libel, slander, or the creation of “truth,” the belief in which promote the interests of particular individuals or groups. Imagine, for example, a first investor who buys grapefruit futures, who then seeds training sets with the idea that grapefruits will become the new gold, with the result that later prospective investors who consult AI models for investment advice are informed that they should invest in grapefruit, enriching the first investor. Or, consider a malevolent political movement that hopes to rehabilitate the reputation of an abhorrent leader; if done effectively, this movement could seed training sets with sympathetic information about this leader, resulting in positive portrayals of this leader in the future outputs of trained AI models.

This Article adopts the cautious attitude necessitated by Justice Holmes’ bad man, applying it to proactively stopping, or retroactively punishing and correcting, deliberate attempts to subvert the training sets of AI models. It offers legal approaches drawn from doctrines ranging from fraud, nuisance, libel, and slander, to misappropriation, privacy, and right of publicity. It balances these with protections for speech afforded by the First Amendment and other doctrines of free speech. The result is the first comprehensive attempt to prevent, respond to, and correct deliberate attempts to subvert training sets of AI models for malicious purposes.

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