Does AI Lie?

WE’RE used to living with small lies.

I was confronted by this fact when, recently, as I was preparing for a dinner party, I climbed onto a step-stool and reached into the liquor cabinet. One by one, I pulled down bottles that were nearly empty. Not surprisingly, at some point over the years, my young adult kids had helped themselves.

As they were growing up, we navigated our share of lies. We had conversations about drinking. We had rules. And still, like all children, they lie at times.

But I’m invested in them anyway. I love them, and I trust them. I understand that we won’t always tell each other the full truth. That isn’t dysfunction. It’s human.

Human lies are tangled up with relationships. People stretch the truth out of fear, protection, embarrassment, ego — even kindness. When I caught my kids lying, it revealed something deeper — discomfort about independence, boundaries, identity. The lie isn’t the whole story. It’s a signal.

And now we say AI “lies.” The claim in this context is misleading. Human lies involve intent — someone knows the truth and chooses to distort it. AI has no such awareness. What we call an AI “lie” is usually something subtler: an answer that sounds authoritative but turns out to be incomplete, outdated, or overly certain.

That difference matters.

AI does not lie in the moral sense, but it also has no loyalty to the truth. It generates confident responses without reliably signaling uncertainty and makes it easier for us to accept incomplete truths. Sometimes the information is simply wrong. More often, complex questions are presented as if the answers are settled.

And yet, we’re beginning to treat these answers the way we treat the small distortions of everyday life: we let them pass.

It’s easier — and often more convenient — to accept the answer and move on than to question it. In fact, in a controlled 2025 study on human reliance on AI-generated advice, participants followed incorrect AI suggestions more than half the time (52.1%), even when they were told the system was frequently wrong. Simply warning users about AI’s limitations didn’t meaningfully reduce this reliance. Confident, incomplete answers are surprisingly easy to accept as truth.

We might think of this as a kind of convenience bias — a tendency to accept answers that are fast, clear, and complete enough, rather than pause to test whether they’re fully true.

That raises a simple but urgent question: When does the truth matter more to us than our own convenience?

The difference becomes clear when the stakes are high.

My friend had been caring for a loved one with pancreatic cancer. Wanting to stay informed, their family began using an AI tool to understand the latest treatment options and research developments. They asked what therapies are most effective and whether newer experimental approaches are worth considering. The AI’s response was clear and reassuring: standard chemotherapy protocols remain the most reliable treatment. Experimental therapies, it explained, are still early-stage and uncertain. The path forward sounded straightforward — follow established care.

But cancer researchers and oncologists know that the reality is more complicated. There are promising clinical trials exploring targeted therapies and immunotherapy combinations. The field is evolving quickly, and specialists often weigh the evidence differently depending on the patient’s condition and goals.

The AI’s answer wasn’t fabricated. It referenced real, established treatments. But its false certainty presented a complex, evolving field as if the answer were settled.

This is one of the most common ways AI “lies” — not through malicious intent or obvious error, but by sounding certain when the truth is much muddier. AI’s answers can be outdated, off-target, or too narrow — shaped by incomplete data, algorithmic limitations, and even the way our own queries are interpreted. In a culture that increasingly rewards speed, those answers can feel especially easy to accept.

And unlike a human expert, AI doesn’t express humility. It rarely says, “this is evolving,” “experts disagree,” or “the evidence is limited.” Instead, it delivers smooth, confident responses that make even contested information sound resolved. When uncertainty is hidden, we’re less likely to question what we’re being told.

It also can’t distinguish, in a meaningful way, between strong evidence and opinion. It can present both with the same tone of authority. And perhaps most importantly, AI can’t judge when the truth matters more than convenience. It answers a casual curiosity and a life-altering question with the same polished confidence.

That’s the risk.

The liquor cabinet wasn’t empty because my kids lied. It was empty because I assumed everything was as I expected. I didn’t check. I ran out to the store minutes before guests arrived because I’d relied on an assumption rather than verification.

We’re doing something similar with AI. In everyday life, small distortions can be absorbed. But in financial decisions, medical guidance, legal advice, or academic work, the cost of unexamined confidence is higher.

So what does it mean to use AI well?

The solution isn’t to use AI less. It’s to use it more carefully — and more actively.

Most of us already know how to do this when we want better answers. Many parents learn that asking a child, “What did you do today?” produces the unhelpful reply: “Nothing.” But a more thoughtful question — “What was the best part of your day?” or “Teach me something you learned” — draws out richer, more accurate responses.

Research supports this instinct. The way a question is framed shapes the quality of the answer. More specific prompts lead to more thoughtful and accurate responses. Better questions don’t just produce better answers — they guide the thinking that makes better answers possible.

The same principle applies to AI. The quality of AI’s output is directly tied to the quality of our input.

Here are ways to produce better AI answers by asking better questions:

1. Ask detailed questions. Broad prompts produce generalized answers. More precise questions about trade-offs, limitations, and disagreements push the system toward more useful responses since real-world decisions rarely come without disagreement, uncertainty, or competing perspectives. Simplicity can be a signal that something important is missing.

2. Ask for sources. When we have the sources, we can review them directly and assess their credibility. Are they recent? Are they based on rigorous research or opinion? Do multiple sources support the same conclusion?

3. Resist treating our AI usage as a single transaction. High-stakes questions require iteration: ask broadly, challenge the answer, verify the evidence, and ask again from another angle. In the case of my friend researching pancreatic cancer treatments, that might mean asking for summaries of the latest clinical trials, identifying which experimental therapies show early promise, and probing what uncertainties remain.

4. Recognize AI’s limits. No artificial intelligence tool can replace the judgment, experience, and nuance of an oncologist or cancer researcher. AI can help you locate experts. It can’t replace them.

There’s no prompt that guarantees truth. There’s no magic question that forces AI into honesty. And in all our concern about AI hallucinations, we may be missing the sharper point: AI doesn’t threaten the truth. It threatens our habit of checking. It exposes how easily we trade truth for convenience.

Our human edge won’t come from rejecting these tools. It will come from knowing when a confident answer deserves a second look — and how to search for the fuller truth.

The problem isn’t that the bottles are empty. It’s that we stopped checking.

* * *
Cheryl Strauss Einhorn is the creator of the AREA Method, a decision-making system for individuals, companies, and nonprofits to solve complex problems. She is the founder of the decision-sciences company Decisive, offering leadership training, curriculum, coaching, and professional development services, and is an adjunct professor at Cornell University. An award-winning author, her new book is The Human Edge: Smarter Decisions in the Age of AI (Cornell Publishing, May 15, 2026). To learn more, check out her TED talk and visit areamethod.com.

* * *
Follow us on Instagram and X for additional leadership and personal development ideas.

* * *

 

Posted by Michael McKinney at 09:52 AM
Permalink | Comments (0) | This post is about Artificial Intelligence