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Can AI Lie? On Hallucinations, Honesty, and Moral Responsibility

Imagine this: you ask an AI model to extract information from a file. The answer sounds convincing — yet the information isn’t there at all.

Does that count as lying? Or is it simply a system trying to produce something meaningful, even when it doesn’t actually know the answer?

We’ve reached a point in AI development where these questions are no longer hypothetical. In fact, the way we handle this technology will determine whether AI becomes a trustworthy partner — or a source of confusion. It’s time to talk about something fundamental: honesty and transparency — not only from AI itself, but also from the people who use it.

What Does ‘Honesty’ Mean in AI?

Honesty works both ways. On the one hand, we expect AI models to be transparent about how they generate their answers — and, more importantly, to admit when they don’t know something. Not to hallucinate just to provide an answer at all costs. We wouldn’t accept that from a person, so why from AI?

On the other hand, users and organizations also bear responsibility. When should you use AI — and when shouldn’t you? And are you being honest about that with colleagues, clients, or partners?

When should you disclose that AI was used?

Not every email needs a disclaimer (“This text was partially generated by ChatGPT”). But as soon as real decisions are being made — think of proposals, advice, or policy documents — it’s proper to acknowledge that AI played a role. Unless you have reviewed, corrected, and fully stand behind the final result. In that case, it has become your own work.

When AI Pretends

One of the greatest risks is that AI models can sound convincing even when they make things up. Think of non-existent sources, summaries of documents never opened, or confident answers based purely on assumptions.

That’s why it’s essential not to use AI as the final authority, but as a partner. Because no — it doesn’t actually know anything. It merely predicts which word is most likely to come next. Once we understand that mechanism, we can use the system more intelligently and safely.

Who Bears the Responsibility?

The interesting (or frustrating) thing about AI is that there is no single point of accountability. Developers build models optimized for completeness — because that’s what the market rewards. Users, in turn, expect AI to know everything. But what if we challenged that expectation?

The Italian philosopher Luciano Floridi calls this distributed moral responsibility: a shared ethical ownership of how technology evolves. When companies, users, and developers each take their role seriously, we create space for more transparent AI — systems that can be wrong, admit it, and still remain trustworthy.

For Companies Just Getting Started with AI

Ethical dilemmas often sound abstract, but they’re surprisingly practical. A few tips if you’re just beginning:

1. Be transparent. Simply say when you’re using AI.

2. Test in a safe environment. Mistakes are part of learning — just make sure they don’t impact your clients.

3. Don’t be afraid to ask “stupid” questions. It’s a strength, not a weakness. That curiosity fuels growth.

Conclusion: Make Transparency the Standard

AI doesn’t have to lie. But it will — unless we set boundaries. By making transparency the norm and routinely reviewing AI output, we make AI safer, fairer, and more valuable.

Are you ready to be honest about your use of AI?

Get in touch! Discover how we can help you use AI responsibly and effectively.