When AI ‘hallucinates’ into a global embarrassment

An article cut-and-pasted from ChatGPT raises questions over the role of fact-checkers in the legacy media

(Also published by RT.com)

In a farcical yet telling blunder, multiple major newspapers, including the Chicago Sun-Times and Philadelphia Inquirer, recently published a summer-reading list riddled with nonexistent books that were “hallucinated” by ChatGPT, with many of them falsely attributed to real authors.

The syndicated article, distributed by Hearst’s King Features, peddled fabricated titles based on woke themes, exposing both the media’s overreliance on cheap AI content and the incurable rot of legacy journalism. That this travesty slipped past editors at moribund outlets (the Sun-Times had just axed 20% of its staff) underscores a darker truth: when desperation and unprofessionalism meets unvetted algorithms, the frayed line between legacy media and nonsense simply vanishes.

The trend seems ominous. AI is now overwhelmed by a smorgasbord of fake news, fake data, fake science and unmitigated mendacity that is churning established logic, facts and common sense into a putrid slush of cognitive rot. But what exactly is AI hallucination?

AI hallucination occurs when a generative AI model (like ChatGPT, DeepSeek, Gemini, or DALL·E) produces false, nonsensical, or fabricated information with high confidence. Unlike human errors, these mistakes stem from how AI models generate responses by predicting plausible patterns rather than synthesising established facts.

Why does AI 'hallucinate'?

There are several reasons why AI generates wholly incorrect information. It has nothing to do with the ongoing fearmongering over AI attaining sentience or even acquiring a “soul.”

Training on imperfect data: AI learns from vast datasets replete with biases, errors, and inconsistencies. Prolonged training on these materials may result in the generation of myths, outdated facts, or conflicting sources.

Over-optimization for ‘plausibility’: Contrary to what some experts claim, AI is nowhere near attaining “sentience” and therefore cannot discern “truth”. GPTs in particular are giant planetary-wide neural encyclopedias that crunches data and synthesises the most salient information based on pre-existent patterns. When gaps exist, it fills them with statistically probable (but likely wrong) answers. This was however not the case with the Sun-Times fiasco.

Lack of grounding in reality: Unlike humans, AI has no direct experience of the world. It cannot verify facts as it can only mimic language structures. For example, when asked "What’s the safest car in 2025?" it might invent a model that doesn’t exist because it is filling in the gap for an ideal car with desired features — as determined by the mass of “experts” — rather than a real one.

Prompt ambiguity: Many GPT users are lazy and may not know how to present a proper prompt. Vague or conflicting prompts also increase hallucination risks. Ridiculous requests like "Summarize a study about cats and gender theory" may result in an AI-fabricated fake study which may appear very academic on the surface.

Creative generation vs. factual recall: AI models like ChatGPT prioritize fluency over accuracy. When unsure, they improvise rather than admit ignorance. Ever came across a GPT answer that goes like this: “Sorry. This is beyond the remit of my training?” (DeepSeek includes this feature)

Reinforcing fake news and patterns: GPTs can identify particular users based on logins (a no-brainer), IP addresses, semantic and syntactic peculiarities and personnel propensities. It then reinforces them. When someone constantly uses GPTs to peddle fake news or propaganda puff pieces, AI may recognise such patterns and proceed to generate content that are partially or wholly fictitious. This is a classic case of algorithmic supply and demand.

Remember, GPTs not only train on vast datasets, it can also train on your dataset.

Reinforcing Big Tech biases and censorship: Virtually every Big Tech firm behind GPT rollouts is also engaged in industrial-scale censorship and algorithmic shadowbanning. This applies to individuals and alternative media platforms alike and constitute a modern-day, digitally-curated damnatio memoriae. Google’s search engine, in particular, has a propensity for up-ranking the outputs of a serial plagiarist rather than the original article.

The perpetuation of this systemic fraud may explode into an outright global scandal one day. Imagine waking up one morning to read that your favourite quotes or works were the products of a carefully-calibrated campaign of algorithmic shunting at the expense of the original ideators or authors? This is the inevitable consequence of monetizing censorship while outsourcing “knowledge” to an AI hobbled by ideological parameters.

Experiments on human gullibility: I recently raised the hypothetical possibility of AI being trained to study human gullibility, in a way conceptually similar to the Milgram Experiment, the Asch Conformity Experiments and its iteration, the Crutchfield Situation. Humans are both gullible and timorous and the vast majority of them tend to conform to either the human mob or in the case of AI, the “data mob.”

This will inevitably have real-world consequences, as AI is increasingly embedded in critical, time-sensitive operations — from pilots’ cockpits and nuclear plants to biowarfare labs and sprawling chemical facilities. Now imagine making a fateful decision in such high-stakes environments, based on flawed AI input? This is precisely why “future planners” must understand both the percentage and personality types of qualified professionals who are prone to trusting faulty machine-generated recommendations.

Fact-checkers didn’t fact-check?

When AI generates an article on one’s behalf, any journalist worth his salt should consider it as having been written by another party and therefore subject to fact-checking and improvisation. As long as the final product is fact-checked, and substantial value, content and revisions are added to the original draft, I don’t see any conflict of interest or breach of ethics involved in the process. GPTs can act as a catalyst, an editor or as a “devil’s advocate” to get the scribal ball rolling.

What happened in this saga was that the writer, Marco Buscaglia, appeared to have wholly cut and pasted ChatGPT’s opus and passed it off as his own. (After this embarrassing episode was exposed, his website went blank and private). The overload of woke-themed nonsense generated by ChatGPT should have raised red flags in the Buscaglia’s mind but I am guessing that he might be prone to peddling this stuff himself.

However all the opprobrium currently directed at Buscaglia should also be applied to the editors of King Features Syndicate and various news outlets who didn’t fact-check the content even as they posed as the bastions of the truth, the whole truth and nothing but the truth. Various levels of gatekeepers simply failed to do their jobs. This is a collective dereliction of duty from the media which casually pimps its services to the high and mighty while it pontificates ethics, integrity and values to lesser mortals.

I guess we are used to such double-standards by now. But here is the terrifying part: I am certain that faulty data and flawed inputs are already flowing from AI systems into trading and financial platforms, aviation controls, nuclear reactors, biowarfare labs, and sensitive chemical plants — even as I write this. The gatekeepers just aren’t qualified for such complex tasks, except on paper, that is. These are the consequences of a world “designed by clowns and supervised by monkeys.”

I will end on a note highlighting the irony of ironies: All the affected editors in this saga could have used ChatGPT to subject Buscaglia’s article to a factual content check. It would have only taken 30 seconds!

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