From subtle disinformation campaigns to the fabrication of political reality, deepfakes are rapidly evolving from a technical curiosity into a severe threat to democratic elections, particularly in the United States. In this newest update of my 2019 article on Deep Fakes, now version 2, I examine these sophisticated new tools and predictive analytics being used to manipulate voter behavior.
I think that it is fairly well-accepted, in the age of generative artificial intelligence (AI), that perceived political reality is no longer a fixed boundary but a variable that can be manipulated in real time. Videos were once considered the ultimate, unalterable proof, but sophisticated synthetic media, or deepfakes, have fundamentally challenged this assumption that seeing is believing. Deepfake technology, which exploits our reliance on visual and audio authenticity, presents one of the most potent threats to the information ecosystem and, consequently, to the integrity of free and fair elections.
Deepfakes: The Next Generation of AI-Generated Fake News
Deepfakes are the zenith of fabricated content, representing the most technologically advanced form of fake news. Technically, deepfakes refer to fake videos, audios, and photos created by artificial intelligence (AI) using a branch of AI called Deep Learning. The creation process primarily relies on generative adversarial networks (GANs), diffusion models, and multimodal systems that learn from real data to render content often indistinguishable from authentic media.
A deepfake becomes a political threat when it alters reality or fabricates statements attributed to real individuals to spread disinformation, create chaos and ultimately change the government and the course of human history. This directly intersects with the core definition of fake news: false stories that are fabricated, lack authentic sources, and are created with the intention to deceive people and manipulate mass opinion. As outlined in my article, Fake News: Everything You Need to Know, disinformation false information spread with harmful intentions is a key ingredient of fake news. Deepfakes are the ultimate tool for disinformation, producing "manipulated content" that can easily bypass human detection and cognitive biases.
The Political Dilemma: How AI Disinformation Undermines Public Trust
While deepfakes can be used for harmless entertainment, the malicious potential poses an existential threat to societal stability and democratic processes. For instance, a fabricated video of a newly-elected U.S. president announcing a sweeping ban on visas or a prominent climate activist praising a major carbon emitter though entirely false can instantly destabilize public discourse and incite a rapid, widespread emotional response.
The primary danger lies not just in the content, but in the ensuing "liar's dividend," where bad actors exploit public confusion to dismiss "authentic" evidence as fake, thereby eroding public trust and allowing them to evade accountability. The long-term consequence of this AI-driven disinformation is a landscape where the truth itself is contested. As I discuss in my analysis, this erosion of objective reality enables political operators, including those who align with the ideologies of Elitists and Neoconservatives, to operate in a low-trust environment where centralized power and specific narratives are easier to enforce. For further context on these key players in the American political sphere, you can refer to my article, Elitists Neocons Neolibs, Globalists and Narcissists, oh my. A U.S. senator once considered deepfakes the modern equivalent of nuclear weapons, capable of throwing a country into tremendous crisis without heavy military power.
The Near-Future Threat: Predictive AI Manipulation and the American Electorate
The fictional BBC series The Capture, particularly Season 2, serves as a powerful analogy for the integration of deepfakes and mass data to systematically manipulate elections, a scenario increasingly plausible in the United States.
The show's premise centers on "Correction," a fictional, real-time deepfake system used by intelligence agencies to manipulate live video feeds. Season 2 escalates the threat by focusing on a Big Tech firm, Truro Analytics, led by Gregory Knox, which possesses a proprietary algorithm capable of predicting voter behavior and influencing political outcomes. The plot involves using this predictive data and the "Correction" deepfake technology to manufacture a favorable political narrative around rising star MP Isaac Turner, ultimately securing his path to becoming Prime Minister. This narrative highlights a potential sophisticated, near-future threat to U.S. and Western elections:
Algorithmic Control and Voter Microtargeting
The manipulation is not merely about creating a single viral deepfake; it's about weaponizing population data to predict what the public wants to hear and then using real-time deepfake technology to deliver that message with uncanny authenticity through fabricated interviews. This reflects the real-world application of AI in microtargeting, where campaigns already use consumer data and social media activity to hyper-personalize messaging, creating "echo chambers" to reinforce pre-existing beliefs and motivate targeted voters.
Erosion of Authentic Political Debate
As seen with the fictional Isaac Turner, the goal is to make the politician's deepfake—the algorithmically-optimized version—the public's preferred candidate. AI-generated content is now capable of producing politically relevant false news that humans often cannot distinguish from real news, rapidly flooding the information sphere with low-cost, algorithmically generated propaganda.
Real-World Precedents of Deepfakes in U.S. Elections
Incidents like AI-generated robocalls impersonating President Biden to discourage primary voting in New Hampshire demonstrate that deceptive AI content is already being deployed for targeted political interference.
The high-stakes Los Angeles mayoral race serves as a vivid recent example of how AI is weaponized in local politics. This is epitomized by the circulation of an offensive (to some) AI-generated video showing Mayor Karen Bass's face superimposed onto a character acting like a deranged, angry Hitler in the movie Downfall. Such an extreme, negative deepfake is designed to achieve a clear political goal: to subvert the election by associating the incumbent with the most universally condemned figure in history, thereby maximizing voter outrage, demobilization, and suppression among those who might support her. The tactic is to flood the information space with toxic, attention-grabbing content, forcing the candidate to use valuable campaign resources to counter a fabricated narrative rather than promote her platform. Furthermore, major AI-related super PACs, funded by tech billionaires, are pouring millions into state and federal races to influence AI policy and support lawmakers in the 2026 midterms and beyond.
The ending of The Capture Season 2, where Detective Inspector Rachel Carey orchestrated the deepfake Isaac Turner to purposefully glitch on a live news broadcast and expose the "Correction" system, provided a moment of democratic resilience. However, the pervasive presence of predictive AI and deepfake technology in the hands of powerful, motivated actors—whether intelligence agencies or Big Tech—suggests that in the near future, the American electorate may face manipulation that is so seamlessly integrated with data-driven microtargeting that only sophisticated AI-assisted countermeasures can reliably uncover the deception.
Detecting Deepfakes: The Limits of Human Instinct
You can spot non-professional deepfake content by looking for clues such as: low resolution, imprecise lip-synching, breaking down of face edges, and abnormal jaw movements. However, modern, high-quality synthetic media are increasingly difficult for humans to detect. Studies show that humans correctly identify high-quality deepfakes only about 24–25% of the time, often hindered by confirmation bias—the tendency to believe content that aligns with pre-existing beliefs.
Therefore, relying on the motto that "seeing is believing" is fundamentally compromised in the digital age. Combating this requires vigilance, robust media literacy for voters, and advanced AI tools to establish content provenance and authenticity.
Mitigating the Risk to Democracy
The future of deepfake technology is one of exponential growth in sophistication and accuracy. The risk to democracy necessitates a multi-faceted and urgent response:
- Transparency and Provenance: Mandatory clear labeling is needed for all AI-generated political ads and deepfakes to enhance transparency and help voters discern authentic information from manipulated media.
- Platform Accountability: Social media platforms must reinvest in trust and safety measures and implement real-time content authenticity scoring to proactively counter the mass production of AI-generated propaganda.
- Regulation and Ethical Guidelines: Governments must establish legal and ethical guidelines for AI developers, focusing on mitigating risks in politically sensitive contexts and holding platforms accountable for disseminating harmful deepfakes.
Frequently Asked Questions About Deepfakes and Elections
What is a deepfake, in simple terms? A deepfake is a fake video, audio clip, or photo generated by artificial intelligence—typically using generative adversarial networks (GANs) or diffusion models—that is designed to look or sound like a real, authentic recording of a real person.
Why are deepfakes considered a threat to elections? Deepfakes can fabricate statements or actions attributed to real politicians and public figures, spreading disinformation that manipulates voter emotion and behavior. They can also be used to discredit genuine evidence via the "liar's dividend," making it harder for voters to trust any media at all.
Can humans reliably detect deepfakes? No. Research indicates that even attentive viewers correctly identify high-quality deepfakes only about 24–25% of the time, largely due to confirmation bias and the increasing sophistication of generative AI tools.
Are there real examples of deepfakes used in U.S. politics? Yes. Examples include AI-generated robocalls impersonating President Biden during the New Hampshire primary and an AI-generated video manipulating footage of Los Angeles Mayor Karen Bass. Both were designed to suppress or influence voter behavior.
How can voters protect themselves from AI-generated disinformation? Voters should rely on media literacy, verify claims through multiple credible sources, look for official content provenance labeling, and remain skeptical of emotionally charged political content that surfaces shortly before an election.
What regulations currently exist for AI-generated political content? Regulation is still evolving. Some U.S. states and the FCC have taken narrow action (such as restricting AI-generated robocall voices), but comprehensive federal legislation specifically addressing political deepfakes remains limited as of this writing—readers should check current state and federal guidance for updates.
Related References
- Detecting Deepfakes: Strategies and Tools
- Fake News: Everything You Need to Know
- Elitists, Neocons, Neolibs, Globalists and Narcissists, Oh My — Why Should I Care?
- AI-Generated Video Showing Mayor Karen Bass — Case Study
- Reducing the Influence of Politics in Healthcare
- Is It Possible to Predict Future Political Events?
(Note: These internal links also appear on the author's Substack — cross-reference there for reader distribution.)
About:
This article is part of an ongoing analytical series on artificial intelligence, digital society, media integrity, and public policy, written by an independent analyst and blogger who covers the intersection of emerging technology, healthcare policy, and American political discourse. The author regularly revisits and updates prior work — as with this V2 update to a 2019 piece — to reflect the rapidly evolving AI landscape. Follow ongoing coverage on this blog and on Substack for related analysis on fake news, disinformation, and technology policy.
Last Updated: August 4, 2026
