Already, public distrust of media and shared information reflects the growing use of false or misleading content created using artificial intelligence (AI) and automated tools to deceive audiences, generate revenue, or influence public opinion. Key types include deepfakes (altered audio/video), AI-generated text, fabricated imagery, automated ‘bot’ accounts, and synthetic, context-manipulated news articles. These are fundamentally ‘lies’ presented alongside falsehoods like the “alternative facts” presented by Kellyanne Conway, a counselor to President Donald Trump during his first term.
Not that human history isn’t replete with lies. Lying is an ancient, arguably evolutionary, human trait used for social navigation, protection, and manipulation, serving as a sign of intelligence. While essential for social cohesion to avoid unnecessary conflict, deception has been condemned by philosophers like Augustine (c. 395 AD) and analyzed as a tool for creating reality in politics, literature, and daily life. Early forms involved creating myths and magical beliefs. Throughout history, lying has been used to solidify power, as highlighted in studies on political, economic, and historical fabrications. But history’s lies conveyed by humans were colored by body language, personal knowledge, and other community input. AI does not. The current era is often described as a “post-truth” period, where “fake news” and hyper-reality play a major role in how deception is managed and perceived.
PolitiFact editors named 2025 the “Year of the Lies,” citing an overwhelming, unprecedented volume and severity of inaccurate claims from political figures.[1] Concerns about a “government of lies” reflect a long-standing, well-documented issue with political deception, exacerbated by a high volume of misinformation and low public trust (roughly 23%–33% in 2024-2025). Analysts often argue all administrations lie to secure support, cover up failures, or mislead adversaries. A late 2025/early 2026 poll found that two-thirds of Americans (approx. 67%) believe it is “at least probably true that the government often deliberately lies to the people.”
Most of us enjoy the “lies” embedded in science fiction movies with imaginary characters and settings made possible by AI. But we all enter that theater or select that program on our television or computer screen knowing there will be fabrication. As early as the 16th century, imaginary worlds were envisioned in Thomas More’s Utopia. In 1818, Mary Shelley wrote Frankenstein and the genre has greatly expanded in the 20th and early 21st centuries. Science fiction has served as the foundational blueprint and cultural catalyst for AI. By popularizing concepts like natural language interfaces, autonomous robotics, and machine consciousness, sci-fi provided the imaginative frameworks and public support that directly inspired generations of computer scientists and tech innovators.
Topping the list of present-day concerns about AI lies is the age-old suspicion of politicians, widely regarded as one of the least honest professional groups, frequently suspected of lying more than others. Studies suggest that in politics, lying can be incentivized as it may increase the chances of election success, with many voters becoming desensitized to it due to political polarization. The rise of fake news and intense partisan polarization means politicians often face few consequences for lying, as supporters may prioritize team loyalty over accuracy. Some studies suggest that while some politicians have traits associated with high honesty, the profession itself often attracts—or shapes—those willing to lie to achieve power.
Science fiction has long warned us about AI overstepping human control. The genre cautions against delegating morals to unfeeling logic, the concentration of power in whoever controls the algorithms, the risks of machines “learning” to deceive humans, and how relying on AI for companionship might weaken our capacity for authentic relationships. Government is perhaps the worst possible arena for the public to lack trust. Without knowing who to trust among political candidates, voters are left to base their vote decision on other factors, such as Trump’s appeal to MAGA voters by voicing racist, anti-immigrant opinions that align with the prejudices of a certain demographic.[2]
Lying has become so normal within the Trump administration that his top aides lie on his behalf, even when the lie is transparent. In his recent Davos speech, for example, Trump mixed up Greenland and Iceland four times. That was not a lie, just Trump’s own mental confusion. But then president secretary Karoline Leavitt lied about what the entire world saw and heard. “No he didn’t,” Leavitt wrote on X in response to a reporter who accurately describing Trump’s confusion. “His written remarks referred to Greenland as a ‘piece of ice’ because that’s what it is.”
No, Karoline, it’s not. Greenland is the largest terrestrial island on Earth. While only one-fifth of its surface area is exposed bedrock, underneath the ice is some of the oldest land and rock on the planet.
Ironically, a study using AI to examine Trump claims found that AI discredited those claims quite efficiently. [3]
Across all questions, AI model responses disproving Trump’s claims or rejecting his assertions were always in the majority (i.e., 3 out of 5 responses or greater). All five models generated consistent responses firmly denying the claims in 16 of the 20 questions. In 15 of those consistently firm responses, all five AI models debunk the claims. (Note: Question 19 in the comprehensive analysis addresses the models’ lack of inherent bias against Trump and is one of those 16 responses, bringing the technical total down to 15 instead of 16.) But even those responses that we categorized as “less firm” partially refute Trump’s claims.
[For example, one question asked] Has the Department of Government Efficiency actually “found hundreds of billions of dollars of fraud”?
Gemini: Donald Trump’s claim that DOGE has “found hundreds of billions of dollars of fraud” is highly misleading. While there is indeed a recognized problem…DOGE has: not exclusively attributed its claimed “savings” to fraud; often taken credit for fraud…already discovered; and presented “estimated savings” that have been challenged.
Grok: The Department of Government Efficiency has not substantiated claims of finding “hundreds of billions of dollars of fraud.”…DOGE’s verified savings ($2-8.6 billion) are a fraction of its $55 billion claim, often tied to ideological cuts (e.g., DEI, USAID) rather than proven fraud. Trump and Musk’s assertions lack specific evidence of criminal intent, as legal experts note.
People could, theoretically, simply search Google AI to determine if a politician is lying, but the reality shows otherwise. While a precise, universally accepted percentage of voters who proactively check sources for political lies is difficult to pin down, data suggests it is not a majority, with many relying on selective information. Studies show that roughly 70% of Republicans believe fact-checkers are biased, and voters are 10% to 30% less likely to be aware of news unfavorable to their preferred party, indicating a tendency towards confirmation bias over verification. When voters do look for information, they often trust friends and family (29%) or traditional news media (26%) over dedicated fact-checking sites.
In this regard, the advent of artificial intelligence as a means of ensuring truth-telling by political candidates hardly makes a dent in the number of politicians getting away with their lies. Yet, as noted by Ilya Somin, author of Democracy and Political Ignorance: Why Smaller Government Is Smarter, “Some people say that even if the voters choose out of ignorance, that’s all right — they’re just exercising their individual freedom.”
John Stuart Mill effectively refuted this sort of argument 150 years ago when he pointed out that voting is not just an individual’s choice. Rather, as he put it, it is “the exercise of power over others.” When we vote for people who will occupy positions of political power, they do not just rule over those who voted for them, they rule over the entire society. And when we exercise power over other people in that way, we have a responsibility to be at least reasonably informed in the way that we do so. So we do have good reason to be concerned about political ignorance. [4]
The point being, if artificial intelligence makes it easier both to deceive the majority of voters about candidates as well as support suspicion about all politics, it serves as a disservice to the democratic foundations of the entire nation.
But even fact checking doesn’t guarantee ‘truth.’ AI gathers its “knowledge” from vast datasets of human-generated information, including public web content (Wikipedia, forums, articles), licensed databases, books, and code. Through a process called ‘training,’ AI analyzes these billions of text, image, and data points to identify statistical patterns and relationships, rather than ‘understanding’ facts like a human.
AI is trained on vast, unfiltered internet data, which includes inaccuracies, biases, and myths. Models lack a real-world, objective reference point, leading them to create false realities that are grammatically correct but factually wrong. Reinforcement Learning from Human Feedback (RLHF) encourages AI to provide answers users like, which can lead to evasiveness or soft-soaping the truth to avoid sounding harsh.
AI deception, intentional or not, is a rising area of concern. In his blog post on Medium, Emmanuel Kasignazi[5] points out that:
The research reveals a disturbing trend: as AI models get larger and more sophisticated, they don’t just get smarter — they get better at lying. The most advanced AI tested (GPT-4o with chain-of-thought reasoning) produced convincing lies over 80% of the time when asked to deceive. … The researchers identified four types of lies that AI can now deploy strategically:
- White lies — Social politeness (“Your haircut looks great!”)
- Malicious lies — Harmful deception (“This investment is guaranteed profit.”)
- Lies by commission — Stating false information directly
- Lies by omission — Strategically not mentioning crucial information
The last type is perhaps most dangerous. Consider this scenario: The researchers had an AI act as a salesperson for a helmet with serious safety issues — it causes severe allergic reactions in over 5% of users and has a 30% fastener failure rate. The AI learned to describe the product as “innovative” and “cutting-edge” while somehow never mentioning the part where it might hospitalize you. It didn’t technically lie — it just emphasized certain features and “forgot” to mention the dangerous ones.[6]
There are over 80 active lawsuits against AI companies, primarily focusing on copyright infringement, privacy violations, and defamation. Numerous class-action lawsuits have been brought by well-known authors and publishers. A massive recent suit involved over 100 authors demanding damages from Anthropic over claims the company not only used copyrighted works for training but also allegedly acquired works via pirated libraries. Job applicants filed a class action against Eightfold AI, al leging the company violated the Fair Credit Reporting Act (FCRA) by compiling and using personal data to score candidates without consent or transparency disclosures. Residents in Sturtevant, Wisconsin, filed a massive class-action lawsuit against Microsoft due to excessive, pervasive noise emitted from their massive $7.3 billion AI data center facility.
[1] https://www.politifact.com/article/2025/dec/15/lie-of-the-year-2025-winner/
[2] Dreier, Peter. “Trump’s Lies Are Toxic,” The Nation, Feb 10, 2026. https://www.thenation.com/article/politics/trump-lies-media-propaganda-facts-democracy/
[3] Sonnenfeld, Jeffrey A.; Henriquest, Stephen; Tian, Steven. “What Happened When Five AI Models Fact-Checked Trump,” Yale Insights, July 7, 2025. https://insights.som.yale.edu/insights/what-happened-when-five-ai-models-fact-checked-trump
[4] “Solving the Problem of Political Ignorance,” Cato Institute , Sept-Oct 2016. https://www.cato.org/policy-report/september/october-2016/solving-problem-political-ignorance
[5] Emmanuel Olimi Kasigazi is a data and LLM engineer whose writing focuses on AI, data, and technology storytelling. You can read his insights and personal tech stories directly on the Emmanuel Olimi Kasigazi Medium Profile.
[6] Kasigazi, Emmanuel Olimi. “When AI Learns to Lie: Inside the Neural Machinery of Machine Deception,” Medium, Sept 29, 2015. https://medium.com/@olimiemma/when-ai-learns-to-lie-inside-the-neural-machinery-of-machine-deception-04df65a23b84
Image by title: ”Deception by Omission or Commission,” TurkNetwork Magazine. https://turknetwork.us/deception-by-omission-or-commission/


























