We Looked at 78 Election Deepfakes. Political Misinformation is not an AI Problem.

AI-generated misinformation was one of the top concerns during the 2024 U.S. presidential election. In January 2024, the World Economic Forum claimed that “misinformation and disinformation is the most severe short-term risk the world faces” and that “AI is amplifying manipulated and distorted information that could destabilize societies.” News headlines about elections in 2024 tell a similar story: In contrast, in our past writing, we predicted that AI would not lead to a misinformation apocalypse. When Meta released its open-weight large language model (called LLaMA), we argued that it would not lead to a tidal wave of misinformation. And in a follow-up essay, we pointed out that the distribution of misinformation is the key bottleneck for influence operations, and while generative AI reduces the cost of creating misinformation, it does not reduce the cost of distributing it. A few other researchers have made similar arguments. Which of these two perspectives better fits the facts? Fortunately, we have the evidence of AI use in elections that took place around the globe in 2024 to help answer this question. Many news outlets and research projects have compiled known instances of AI-generated text and media and their impact. Instead of speculating about AI’s potential, we can look at its real-world impact to date. We analyzed every instance of AI use in elections collected by the WIRED AI Elections Project, which tracked known uses of AI for creating political content during elections taking place in 2024 worldwide. In each case, we identified what AI was used for and estimated the cost of creating similar content without AI. We find that (1) half of AI use isn't deceptive, (2) deceptive content produced using AI is nevertheless cheap to replicate without AI, and (3) focusing on the demand for misinformation rather than the supply is a much more effective way to diagnose problems and identify interventions. To be clear, AI-generated synthetic content poses many real dangers: the creation of non-consensual images of people and child sexual abuse material and the enabling of the liar’s dividend, which allows those in power to brush away real but embarrassing or controversial media content about them as AI-generated. These are all important challenges. This essay is focused on a different problem: political misinformation.1 Improving the information environment is a difficult and ongoing challenge. It’s understandable why people might think AI is making the problem worse: AI does make it possible to fabricate false content. But that has not fundamentally changed the landscape of political misinformation. Paradoxically, the alarm about AI might be comforting because it positions concerns about the information environment as a discrete problem with a discrete solution. But fixes to the information environment depend on structural and institutional changes rather than on curbing AI-generated content. We analyzed all 78 instances of AI use in the WIRED AI Elections Project (source for our analysis).2 We categorized each instance based on whether there was deceptive intent. For example, if AI was used to generate false media depicting a political candidate saying something they didn't, we classified it as deceptive. On the other hand, if a chatbot gave an incorrect response to a genuine user query, a deepfake was created for parody or satire, or a candidate transparently used AI to improve their campaigning materials (such as by translating a speech into a language they don't speak), we classify it as non-deceptive. To our surprise, there was no deceptive intent in 39 of the 78 cases in the database. The most common non-deceptive use of AI was for campaigning. When candidates or supporters used AI for campaigning, in most cases (19 out of 22), the apparent intent was to improve campaigning materials rather than mislead voters with false information. We even found examples of deepfakes that we think helped improve the information environment. In Venezuela, journalists used AI avatars to avoid government retribution when covering news adversarial to the government. In the U.S., a local news organization from Arizona, Arizona Agenda, used deepfakes to educate viewers about how easy it is to manipulate videos. In California, a candidate with laryngitis lost his voice, so he transparently used AI voice cloning to read out typed messages in his voice during meet-and-greets with voters. Reasonable people can disagree on whether using AI in campaigning materials is legitimate or what the appropriate guardrails need to be. But using AI for campaign materials in non-deceptive ways (for example, when AI is used as a tool to improve voter outreach) is much less problematic than deploying AI-generated fake news to sway voters. Of course, not all non-deceptive AI-generated political content is benign.3 Chatbots often incorrectly answer election-related questions. Rather than deceptive intent, this results from the limitations of chatbots, such as hallucinations and lack of factuality. Unfortunately, these limitations are not made clear to users, leading to an overreliance on flawed large language models (LLMs).4 For each of the 39 examples of deceptive intent, where AI use was intended to make viewers believe outright false information, we estimated the cost of creating similar content without AI—for example, by hiring Photoshop experts, video editors, or voice actors. In each case, the cost of creating similar content without AI was modest—no more than a few hundred dollars. (We even found that a video involving a hired stage actor was incorrectly marked as being AI-generated in WIRED’s election database.) In fact, it has long been possible to create media with outright false information without using AI or other fancy tools. One video used stage actors to falsely claim that U.S. Vice President and Democratic presidential candidate Kamala Harris was involved in a hit-and-run incident. Another slowed down the vice president's speech to make it sound like she was slurring her words. An edited video of Indian opposition candidate Rahul Gandhi showed him saying that the incumbent Narendra Modi would win the election. In the original video, Gandhi said his opponent would not win the election, but it was edited using jump cuts to take out the word “not.” Such media content has been called “cheap fakes” (as opposed to AI-generated “deepfakes”). There were many instances of cheap fakes used in the 2024 U.S. election. The News Literacy Project documented known misinformation about the election and found that cheap fakes were used seven times more often than AI-generated content. Similarly, in other countries, cheap fakes were quite prevalent. An India-based fact checker reviewed an order of magnitude more cheap fakes and traditionally edited media compared to deepfakes. In Bangladesh, cheap fakes were over 20 times more prevalent than deepfakes. Let’s consider two examples to analyze how cheap fakes could have led to substantially similar effects as the deepfakes that got a lot of media attention: Donald Trump’s use of Taylor Swift deepfakes to campaign and a voice-cloned robocall that imitated U.S. President Joe Biden in the New Hampshire primary asking voters not to vote. Trump’s use of Swift deepfakes implied that Taylor Swift had endorsed him and that Swift fans were attending his rallies en masse. In the wake of the post, many media outlets blamed AI for the spread of misinformation. But recreating similar images without AI is easy. Images depicting Swift’s support could be created by photoshopping text endorsing Trump onto any of her existing images. Likewise, getting images of Trump supporters wearing “Swifties for Trump” t-shirts could be achieved by distributing free t-shirts at a rally—or even selectively reaching out to Swift fans at Trump rallies. In fact, two of the images Trump shared were real images of a Trump supporter who is also a Swift fan. Another incident…