Artificial Intelligence Doesn't Lie With Intent

We’ve all been there: An artificial intelligence (AI) chatbot delivers us a super-confident, totally polished answer, and we find out later that it made the whole thing up. Maybe it quoted a study that doesn’t exist. Maybe it twisted someone’s words. Whatever the case, the result is the same: It sounds right, but it’s dead wrong. People are calling these slip-ups “AI hallucinations.” It’s a term that’s gained traction now that AI is everywhere. But does “hallucination” really capture what’s happening? Or is it something that, if a person did it, we’d likely call lying?

“Hallucination” Sounds Harmless

At first, calling it a “hallucination” makes it seem like the AI just had a weird moment. Oops, no harm done. But what if that made-up information shows up in a doctor’s advice? Or a school paper? Or a legal document? Now it’s not just a harmless glitch; it’s a real problem. The issue isn’t just that it’s wrong. It’s that it sounds right. And most people don’t think twice before trusting it.

Can an AI Lie?

Technically, no. AI doesn’t think or feel, and it doesn’t plan to trick anyone. It doesn’t know what’s true or false, so by the usual definition, it can’t lie. But here’s where it gets messy: It still spits out wrong answers with total confidence. And they sound so believable that people just go with it. So even if there’s no intent behind it, it feels like a lie. And that’s what makes it so risky.

Researchers like Sun et al. (2024) call this the creation of “false realities”. These are outputs that are wrong, but convincing. The danger lies in how believable these falsehoods are, especially when users assume that AI is a reliable source of truth.

Why Does This Happen?

A big reason has to do with how these systems are built. As Barros (2025) points out, models like GPT aren’t designed to tell the truth; they’re designed to predict the next most likely word. That’s it. They’re trained to be fluent, not accurate. So if the training data has patterns that look like truth, the model will follow them even if they’re wrong. Šekrst (2025) adds a fascinating angle. She argues that AI’s mistakes aren’t gaps in knowledge; they’re more like simulations of belief. In other words, the AI doesn’t believe what it’s saying (because it doesn’t believe anything), but it produces language that mimics belief so well, we respond to it as if it had one.

Why It Matters

When it’s just casual conversation, a hallucination might be funny or weird. But in high-stakes contexts, such as health, law, and science, the cost of being wrong is real. And when AI speaks with polished confidence, it can be hard to tell when it is wrong.

Wu (2024) makes an interesting comparison to people. Humans sometimes stretch the truth to persuade. The difference? We usually have reasons. AI doesn’t. It just keeps generating what sounds good. It’s not being clever. It’s doing what it was trained to do: make the next word sound right.

And this brings us to another crucial point. As humans, we naturally assume that anything that talks like a person thinks like one, too. That’s called anthropomorphism, and it’s a big reason why these systems can be so misleading. As Maleki, Padmanabhan, and Dutta (2024) argue, calling these errors “hallucinations” actually downplays how easily they trigger our cognitive biases.

So What Do We Do?

If AI doesn’t mean to lie but still misleads us, how do we fix it?

Shifting the focus might help. Instead of asking whether AI intended to deceive (it didn’t), we should ask: What impact does this output have? If people are being misinformed or harmed, the intention doesn’t matter.

AI is not a brain; it is a tool. Instead of asking what it “means” or what it “thinks,” ask the question: Can we trust it? How often does it mess up? Can we catch those mistakes before they cause problems? That might mean tying AI’s answers to real, checkable sources so it can’t just make stuff up. It also means being upfront when we’re not sure. And maybe most importantly, it means rethinking what makes AI “good.”

Final Thought

Calling these mistakes “hallucinations” might sound poetic. But it glosses over something more serious. These aren’t just dreamy glitches; they’re structural flaws baked into how AI works. The model isn’t trying to mislead anyone, but that doesn’t make its mistakes any less dangerous. So yes, we can be impressed by how far AI has come. But that doesn’t mean we should trust it without question. Not yet.