Ai Generated Content In American Politics Wikipedia

Content made with generative artificial intelligence has been used in American politics. Generative AI has increased the efficiency with which political candidates were able to raise money by analyzing donor data and identifying possible donors and target audiences.[1][clarification needed] A Democratic consultant working for Dean Phillips has admitted to using AI to generate a robocall which used Joe Biden's voice to discourage voter participation.[2] In April 2023, the Republican National Committee released an attack ad made entirely with AI-generated images depicting a dystopian future under Joe Biden's re-election.[3] In August 2024, The Atlantic noted that AI slop was becoming associated with the political right in the United States, who were using it for shitposting and engagement farming on social media, with the... The last decade taught us painful lessons about how social media can reshape democracy: misinformation spreads faster than truth, online communities harden into echo chambers, and political divisions deepen as polarization grows.

Now, another wave of technology is transforming how voters learn about elections—only faster, at scale, and with far less visibility. Large language models (LLMs) like ChatGPT, Claude, and Gemini, among others, are becoming the new vessels (and sometimes, arbiters) of political information. Our research suggests their influence is already rippling through our democracy. LLMs are being adopted at a pace that makes social media uptake look slow. At the same time, traffic to traditional news and search sites has declined. As the 2026 midterms near, more than half of Americans now have access to AI, which can be used to gather information about candidates, issues, and elections.

Meanwhile, researchers and firms are exploring the use of AI to simulate polling results or to understand how to synthesize voter opinions. These models may appear neutral—politically unbiased, and merely summarizing facts from different sources found in their training data or on the internet. At the same time, they operate as black boxes, designed and trained in ways users can’t see. Researchers are actively trying to unravel the question of whose opinions LLMs reflect. Given their immense power, prevalence, and ability to “personalize” information, these models have the potential to shape what voters believe about candidates, issues, and elections as a whole. And we don’t yet know the extent of that influence.

Published online by Cambridge University Press: 09 October 2025 Artificial Intelligence (AI) was seemingly everywhere by the end of 2024, and the 2024 US presidential election was the first American national election to be conducted wholly in an AI era. Nevertheless, relatively little is known about how effectively generative AI contributes to learning about politics. This study explores that question in the context of research on subnational US politics. Based on a novel methodology that combines the analysis of AI-generated profiles on several US states with interviews with state-level experts, this article identifies and analyses a prevalent national bias in the state-level content... This bias is both a consequence of and a contributor to the problem of the nationalization of American politics, which itself undermines the principles of federalism that undergird Madisonian democracy in the United States.

The 2024 US presidential election was the first US national election contest to take place in the age of generative artificial intelligence (AI).Footnote 1 Generative AI platforms rely on vast amounts of text to... As Romero, Reyes, and Kostakos explain, “This technology processes human inputs, commonly known as prompts, and generates outputs that closely mimic human-generated content, predominantly in the form of text and images.”Footnote 2 By late... Despite its growing prevalence, relatively little is known about how effectively generative AI contributes to learning about politics. The 50 States or Bust! Project, data from which form the basis of this article, was launched in June 2023 in order to contribute to an understanding of the value of politically focussed AI-generated content for understanding subnational US... The question animating the project was, to what extent is ChatGPT useful as a research tool for exploring state- and territory-level US politics?

To answer that question, in June 2023 politically focussed profiles were generated via standardized ChatGPT4 prompts for all US states and territories. Since then, the project team has conducted interviews with experts on the politics of nineteen US states. The interviews ask the experts to assess the content of the AI-generated profiles in order to gauge how effective ChatGPT is at analysing state-level politics.Footnote 3 With the caveat that efforts to secure interviews... The project has further demonstrated the difficulty that generative AI has with accurately citing the sources that it references, including the propensity of AI to “hallucinate” – that is, to simply make up sources... Notably, the expert interviews also reveal a prevalent nationalization bias in the state-level profiles, which has not previously been explored and is the focus of the remainder of this article. While the US Constitution established a federalized republic in which separate governments at the state and national levels would operate, today the distinction between these levels of government has been diminished by a half-century...

Carson, Sievert, and Williamson define nationalization as “a phenomenon in which top-down forces, such as presidential vote choice or partisanship, inform voters’ decisions in subnational elections rather than candidate-specific factors or local forces.”Footnote 5... What is more, Americans’ engagement with state and local politics has declined sharply, a trend that has unfolded more consistently over decades.”Footnote 7 The consequence of nationalization is not simply a benign shift away... Rather, recent scholarship reveals that nationalization exacerbates political polarization and upends the Madisonian state–federal balance of power upon which the US federal system rests.Footnote 8 Co-hosts Archon Fung and Stephen Richer look back at the last five months of headlines as they celebrate the twentieth episode of Terms of Engagement. Archon Fung and Stephen Richer are joined by Michelle Feldman, political director at Mobile Voting, a nonprofit, nonpartisan initiative working to make voting easier with expanded access to mobile voting. Archon Fung and Stephen Richer discuss whether fusion voting expands representation and strengthens smaller parties—or whether it muddies party lines and confuses voters.

Creating a healthy digital civic infrastructure ecosystem means not just deploying technology for the sake of efficiency, but thoughtfully designing tools built to enhance democratic engagement from connection to action. Public engagement has long been too time-consuming and costly for governments to sustain, but AI offers tools to make participation more systematic and impactful. Our new Reboot Democracy Workshop Series replaces lectures with hands-on sessions that teach the practical “how-to’s” of AI-enhanced engagement. Together with leading practitioners and partners at InnovateUS and the Allen Lab at Harvard, we’ll explore how AI can help institutions tap the collective intelligence of our communities more efficiently and effectively. The year 2024 began with bold predictions about how the United States would see its first artificial intelligence (AI) election. 1 Commentators worried that generative AI — a branch of AI that can create new images, audio, video, and text — could produce deepfakes that would so inundate users of social media that they...

2 Meanwhile, some self-labeled techno-optimists proselytized how AI could revolutionize voter outreach and fundraising, thereby leveling the playing field for campaigns that otherwise could not afford expensive political consultants and staff. 3 As the election played out, AI was employed in numerous ways: Foreign adversaries used the technology to augment their election interference by creating copycat news sites filled with what appeared to be AI-generated fake... 4 Campaigns leveraged deepfake technology to convincingly imitate politicians and produce misleading advertisements. 5 Activists deployed AI systems to support voter suppression efforts. 6 Candidates and supporters used AI tools to build political bot networks, translate materials, design eye-catching memes, and assist in voter outreach.

7 And election officials experimented with AI to draft social media content and provide voters with important information like polling locations and hours of operation. 8 Of course, AI likely was also used during this election in ways that have not yet come into focus and may only be revealed months or even years from now. Were the fears and promises overhyped? Yes and no. It would be a stretch to claim that AI transformed U.S. elections last year to either effect, and the worst-case scenarios did not come to pass.

9 But AI did play a role that few could have imagined a mere two years ago, and a review of that role offers some important clues as to how, as the technology becomes... elections — and American democracy more broadly — in the coming years. AI promises to transform how government interacts with and represents its citizens, and how government understands and interprets the will of its people. 10 Revelations that emerge about AI’s applications in 2024 can offer lessons about the guardrails and incentives that must be put in place now — lest even more advanced iterations of the technology be... elections and democratic governance as a whole. This report lays out the Brennan Center’s vision for how policymakers can ensure that AI’s inevitable changes strengthen rather than weaken the open, responsive, accountable, and representative democracy that all Americans deserve.

Now is the time for policymakers at all levels to think deliberately and expansively about how to minimize AI’s dangers and increase its pro-democracy potential. That means more than just passing new laws and regulations that relate directly to election operations. It also includes holding AI developers and tech companies accountable for their products’ capacities to influence how people perceive facts and investing in the resources (including workforces and tools) and audit regimes that will... Policymakers should also establish guardrails for election officials and other public servants that allow them to use AI in ways that improve efficiency, responsiveness, and accountability while not inadvertently falling prey to the technology’s...

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