Risks of generative AI: bias and “hallucinations”

When generative AI landed in anyone’s hands in 2022, the debate flared up immediately. On one hand, it became clear that these tools could draft, summarize, and respond with impressive fluency. On the other, answers started appearing that sounded perfect but were wrong: made-up data, fake citations, and confident claims about things that never happened. That phenomenon got a name that became famous: “hallucinations.”

That debate is still alive, and for a Mexican small business it’s not an abstract topic. If you’re going to use AI to serve customers, draft quotes, or make decisions, you need to understand two concrete risks: bias (when the tool reproduces prejudices or unfair assumptions) and hallucinations (when it invents information with total poise). The good news is that neither is a reason to give up on the technology. They’re a reason to use it well.

What a “hallucination” is and why it happens

A hallucination occurs when the model generates an answer that sounds coherent and confident, but simply isn’t true. It doesn’t do it out of bad faith: these tools don’t “look up” a truth, they predict the most probable next word from patterns. When they don’t have the exact fact, they often fill it in with something that looks correct.

For a business this has real consequences. Imagine an assistant that gives a customer a price that doesn’t exist, cites a made-up warranty policy, or claims that a procedure is done a certain way when it actually doesn’t apply. The problem isn’t that it makes a mistake; it’s that it makes a mistake without warning and with a confidence that invites you to believe it.

Bias: when the tool inherits assumptions

Bias is quieter. Because these models learn from enormous amounts of text, they also absorb the prejudices and imbalances present in that data. That can translate into answers that favor certain profiles, that take for granted cultural assumptions that don’t apply to your clientele, or that treat differently situations that should be treated the same.

• In customer service: a tone or a recommendation that doesn’t fit your real audience.
• In screening or filtering: prioritizing candidates or cases for reasons that shouldn’t carry weight.
• In content: reinforcing stereotypes without anyone noticing until it’s already published.
• In internal decisions: presenting a single perspective as if it were the only valid one.

The key isn’t to distrust, it’s to verify

The most common mistake is treating AI as an infallible oracle or, at the other extreme, dismissing it entirely. Neither stance serves you. Generative AI is a great assistant for speeding up work, as long as there’s a human verification step where it matters.

Generative AI is a brilliant, very self-assured assistant; your job isn’t to believe it blindly, it’s to decide when and in what to trust it.

In practice, this means reserving human judgment for what has consequences: prices, commitments to customers, legal or financial data, and any information that goes out under your company’s name. For low-risk tasks—a first draft, an idea, an internal summary—you can give it more freedom. The difference lies in distinguishing where an error is a nuisance and where it’s a serious problem.

Your first AI case without the scares

If you want to bring generative AI into your small business without exposing yourself to these risks, we recommend starting with something small and with clear results:

• Choose a low-risk case first. Drafting first versions, summarizing emails, or generating ideas rather than decisions with direct impact on the customer.
• Feed the AI with your own data. Custom software can rely on your catalog, your prices, and your real policies, instead of letting the tool improvise. That way you keep your data, processes, and history as your source of truth.
• Define a human verification step. Have a person review what’s sent to customers or used to decide, especially at the start.
• Document the errors you see. Every mistake is information to adjust and improve the system over time.
• Measure the result. Time saved, quality of responses, customer satisfaction: without numbers, you won’t know whether it’s working.

Generative AI isn’t magic or a threat: it’s a powerful tool that needs judgment to deliver good results. Used with good design and a verification step at the important moments, it helps you work faster and decide on information you can actually stand behind. At Normandia Web we design exactly that kind of custom solution, with human verification in the right place: tell us which task you’d like to try first and we’ll build it with you, so you get the best of AI without believing everything blindly.