AI is getting smarter – and it’s also getting better at fabricating stories. If you've ever believed something it said, you might have fallen victim to an "AI hallucination" without realizing it.
"ChatGPT once fabricated a research paper that sounded incredibly plausible. I almost sent it to a client without double-checking," recalled WHO, a freelance marketing content creator . He uses AI almost every day to write articles, describe products, and create SEO content. "It was wrong, but so confident, so sometimes I became complacent. If it flows smoothly, I just believe it."
This situation is not unique. Nguyễn Hữu Phương (21), an English education student in Hanoi, is also accustomed to using ChatGPT for grammar checks and review. "It gives answers faster than Google and is easier to understand than books. But one time, I gave an example to my teacher, and she said it was wrong. That’s when I realized ChatGPT can also... fabricate."
In technical fields, AI errors can be even more dangerous. A Human (28), a software engineer, recounted an instance where AI suggested a piece of code for data processing. "It was syntactically correct, the functions were right, it seemed perfect, but the logic was completely wrong. If I hadn't tested it thoroughly, I would have pushed the error into the live system."
Three stories from three different fields, but they all share one thing in common: AI gave wrong answers, but so convincingly that users believed them. As artificial intelligence becomes more widely used – for writing articles, creating slides, research, programming, and even medical assistance – the phenomenon of "AI fabricating stories like the truth" is becoming a worrying issue. Experts call it AI hallucination.
AI Creates Incorrect Information That Sounds Believable
With large language models (LLMs), hallucinations are typically information that seems convincing but is actually inaccurate, fabricated, or irrelevant. For example, a chatbot might create a scientific reference that sounds very credible – but in reality, it doesn’t exist.
A typical example occurred in 2023, when a lawyer in New York submitted a legal brief drafted with the help of ChatGPT. The judge later discovered that the brief cited a case… that didn’t exist. If not for the discovery, this could have had serious consequences in court.
In the case of AI processing images, hallucinations happen when the system assigns incorrect descriptions to photos. For example, when AI is given an image of a woman talking on the phone, but the system describes it as "a woman sitting on a bench and talking on the phone." Such seemingly small inaccuracies can have major consequences if applied in contexts that require high accuracy.
AI is built by collecting vast amounts of data and identifying patterns within that data. From there, it learns how to answer questions or perform tasks.
For instance, if you provide AI with 1,000 images of different dog breeds, it will learn to differentiate between a poodle and a golden retriever. But if you input a picture of a blueberry muffin – as researchers have done – AI might "identify" it as a chihuahua.
The reason is that when the system doesn't truly understand the question or the information provided, it will "guess" based on similar patterns in its training data. If that data is biased or incomplete, AI will fill in the gaps with flawed reasoning, leading to the phenomenon of hallucination.
It’s important to distinguish AI hallucinations from intentional creativity. If AI is asked to create something, such as writing a story or generating art, then novel outputs are expected. However, if AI is expected to provide accurate information and ends up "fabricating" something that sounds very true, that’s a serious problem.
When Hallucinations Are No Longer Harmless
Calling a muffin a chihuahua might seem amusing. But if a self-driving car misidentifies a pedestrian, the consequences could be a fatal accident. If a military drone misidentifies a target, civilians’ lives could be at risk.
In speech recognition technology, hallucinations can cause AI to "hear" words that were never spoken, especially in noisy environments. For example, the sound of a truck passing by or a baby crying could cause the system to "insert" words into the transcript. If applied in healthcare, law, or social services, these errors could lead to serious consequences.
Although AI companies have made efforts to reduce hallucinations by improving training data and applying control measures, this issue still exists in many popular AI tools.
Therefore, users must always be vigilant when using AI, especially in fields that require absolute accuracy. Always double-check the information provided by AI, cross-check with reliable sources, and don’t hesitate to consult an expert if necessary.
