Plausible Justification, Not Truth — The Main Danger of AI

Artificial intelligence has already learned to speak, write, draw, program, and even produce “reasoned” conclusions convincingly.

But what it generates is not always true. AI operates perfectly within the concept of plausible justification — that is, it creates texts that appear logical, persuasive, and even expert, yet may be completely false.

Plausible justification, not truth, is the main danger of AI.

People trust style, not substance. AI writes with confidence, and this creates the illusion of authority. Even specialists sometimes fail to notice the substitution of facts.

Errors multiply exponentially. If falsehoods are taken as the basis for new models, the system “learns” from disinformation.

The risk areas are media, analytics, and politics. Chatbots are capable of “proving” any thesis, shaping a distorted perception of reality in society.

With just one plausible publication by an AI chatbot from any developer, it is possible to completely discredit a user who publishes something under their own name rather than under the chatbot’s. That is why all chatbots include a polite disclaimer, such as: “ChatGPT may make mistakes. Check important information.”

We can distinguish two groups of errors:

1. Obvious errors

2. Non-obvious errors

Examples of obvious errors:

• In images, instead of accurate text, small-font “hallucinations” appear.

• Instead of five fingers on a hand (4+1), only four are shown (3+1).

• And others.

Examples of non-obvious errors:

• A relatively small number of sources were used for training the AI chatbot.

• Incorrect semantic inference, despite the chatbot’s assurances of truthfulness — and this can repeat several times, even after self-criticism in response to user feedback.

• Particularly dangerous are subtle errors in infographics, such as replacing a single Ukrainian letter with an English one.

• When free usage limits are exceeded, the number of erroneous responses may increase.

Another situational example for this blog: AI chatbots translate “Business Intelligence” as “Бізнес-аналітика” (“Business Analytics” in Ukrainian), but then the logical question arises: how should “Business Analytics” (BA) be translated?

In any case, it is important to understand that an AI chatbot is not a God-created intelligent being, but a human-created network-based machine learning (ML) system — with a human teacher.

What to Do — Conclusions

• Verify sources. Do not trust “intelligent” text without verification. AI is a tool, not the ultimate truth.

• Use critical thinking. If a statement looks “too logical,” that is already a reason to doubt it.

• Develop systems of trust in data. In the future, the key will not be a “smarter” AI, but more reliably controlled data from which it learns.

Would you like me to also prepare a shortened, punchy version of this translation for LinkedIn or Blogger publication, so it works as both an article and a shareable post?