AI Hallucinations: Why They Happen and Why It Might Be Your Fault
For years, many of us treated AI like a quirky, fun toy. It gave confident answers—sometimes useful, sometimes laughably wrong—but always fascinating. The problem? These “hallucinations” aren’t just bugs. They’re part of how AI works. And according to a new paper published by OpenAI, they’re partly our fault.
What Exactly Is an AI Hallucination?
A hallucination in artificial intelligence is when you ask the model a question and it generates an answer that sounds confident, structured, and believable—but is factually wrong. Think of it as your AI best friend making stuff up with a straight face.
Here’s why: large language models don’t work like humans. They don’t think in terms of “if-then” logic. Instead, they predict text by analyzing enormous amounts of patterns in language. For example, if you type the word “fart,” the AI looks at millions of examples of how people have used that word before and after, then predicts what usually comes next. The prediction might be statistically plausible—but not necessarily correct.
That’s why you sometimes get bizarrely wrong answers. Ask, “Why do my farts smell like gasoline?” and the AI might confidently spit out an explanation that makes no sense. The model isn’t reasoning—it’s predicting. And predictions can drift into nonsense.
So, hallucinations are not signs of “bad AI” as much as they are proof of how these systems actually function. They’re a feature of the prediction game.
Why It’s (Partly) Our Fault
Now, why are hallucinations your fault? Or at least, why are they humanity’s fault? The answer lies in incentives.
Think back to school multiple-choice tests. When you didn’t know the answer, did you leave it blank—or did you guess? Most of us guessed. The incentive was always to try rather than admit “I don’t know.”
AI models are trained in a similar way. Their training rewards them for producing outputs—not for admitting ignorance. “I don’t know” isn’t sexy. People want answers, even if they’re wrong. And the internet, which trains these models, is full of humans confidently saying things they don’t fully understand. The result? Models are incentivized to fill the gap rather than leave it empty.
Add to this the way people interact with AI. We often reward models when they’re funny, quirky, or entertaining, even if inaccurate. That “quirky and fun” personality is addictive. But every time we praise or use those answers without checking, we reinforce the problem.
So yes, hallucinations are the AI’s fault—but they’re also a mirror reflecting our own bad habits: preferring guesses over silence.
What the Data Tells Us
OpenAI’s recent paper compared two models: GPT-4-mini and an older GPT-3.5-like version. The results were surprising.
Abstention rate (when the model says “I don’t know”):
Newer model: 52%
Older model: 1%
This means the newer model is much more willing to admit ignorance.
Accuracy rate (correct answers):
Newer model: 22%
Older model: 24%
Not great. The newer model is slightly less accurate.
Error rate (confidently wrong answers):
Newer model: 26%
Older model: 75%
Huge improvement. The newer model makes fewer blatant errors.
What does this mean? Essentially, we’re trading some accuracy for humility. The newer models are better at saying, “I don’t know,” which reduces the risk of being confidently misled. Still, 22% accuracy isn’t something to brag about. It shows that AI remains limited and requires human verification.
The key takeaway: progress is happening, but hallucinations aren’t going away. They’re being managed differently—through abstention rather than blind guessing.
How to Reduce AI Hallucinations
If hallucinations are baked into how AI works, can we fix them? Not entirely—but we can reduce them.
The main solution is to incentivize models to say “I don’t know.” Instead of rewarding them for guessing, we penalize them when they give wrong answers and reward them when they abstain. Narrow, well-defined tasks also help. The more specific the job, the less room for hallucination.
For example, telling a model:
“Analyze this PDF and tell me five reasons it’s great” → too broad, likely to hallucinate.
“Look at the first paragraph on page one and check if it contains the word ‘in’” → narrow, accurate, and efficient.
In practice, that means using AI for smaller, well-scoped tasks rather than big, open-ended questions. The narrower the task, the more reliable the result.
Developers (myself included) are already applying this approach. In one of our projects, an AI app we built kept generating cheerful but wrong answers. We had to retrain it to prefer “I don’t know” instead of guessing. It was frustrating, but it worked.
The lesson? AI thrives when tasks are small and clear. When tasks are broad and fuzzy, hallucinations are inevitable.
Conclusion
AI hallucinations aren’t just random glitches—they’re part of the system. They happen because of how language models are trained, because of how humans interact with them, and because we reward confident answers over honest uncertainty.
The fix isn’t to expect perfect accuracy but to adapt. Use AI for narrow, well-defined jobs. Encourage it to admit ignorance. And always double-check important answers with your own research.
The AI boom might not burst, but it will certainly evolve. The future belongs not to models that always guess but to models that know when to stay silent.
Let’s get to know each other!