Turning Fake Data into Fake News: The AI Training Set as a Trojan Horse of Misinformation

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 forthe "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 lawsshould 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 lead