Neutrality Without Neutral Models

Political neutrality in AI is impossible. What does that mean for the effects of AI on political persuasion? My research on information control in China offers an unexpected vantage point for examining the politics of generative AI. Biased models can persuade, much as propaganda does in China and other authoritarian regimes. But persuasion requires exposure. In competitive information environments, exposure is contested. A large body of research on political campaigns finds small, and often null, effects of advertising, canvassing, and other persuasion attempts, despite enormous resources devoted to them, in environments where people encounter many competing messages. Understanding that exposure, not persuasiveness, is what limits media effects changes how we should approach political neutrality in AI. Neutrality may be unattainable in any single model, but it can be approximated at the level of the ecosystem, through diverse models, viewpoints, and sources. What the Chinese government has done, in its efforts to control digital information and now in generative AI, is restrict diversity at the ecosystem level. This suggests that the central political risk of AI is not that any single model is biased, but that the ecosystem becomes politically homogeneous.