Why Neural Networks Confidently Make Mistakes — and How to Check AI's Answer in 5 Minutes
The author shares observations on how neural networks can give erroneous answers and proposes methods for quickly verifying their accuracy. Situations are discussed where caution is especially warranted...
Краткий пересказ от QRazy ИИ
- Understand the concept of AI hallucinations—erroneous answers presented as accurate facts.
- Identify key facts and dependencies that require verification to avoid misunderstandings.
- Learn in which fields, such as law and medicine, it is particularly important to verify information provided by neural networks.
You ask the neural network what documents are needed to obtain a service and receive a confident answer: five points with explanations. Later, it turns out that one document relates to a different procedure, the second is no longer required, and the requirement to provide the third never existed.
The neural network is not trying to deceive the user. It creates plausible text that may not correspond to reality.
What Are AI Hallucinations?
A hallucination is an erroneous or fabricated answer presented as reliable. The neural network may confuse dates, attribute a non-existent quote to a person, invent research, or combine information from different sources into a coherent but incorrect version.
The language model predicts which words should follow one another. It finds patterns in large volumes of text and, based on them, forms a response. However, the coherence of the text does not guarantee its accuracy.
The model does not possess the human feeling of "I know this for sure" or "the data is insufficient." A confidently formulated question can often yield a equally confident answer, even if part of the information is fabricated.
The risk of error is particularly high when it comes to a niche topic, fresh data, local rules, a specific document, or a situation with incomplete context. Therefore, the persuasiveness of the answer is not a reason to forgo verification.
How to Verify an AI's Answer in Five Minutes
For most everyday and work-related questions, a few steps are sufficient.
Identify the Main Assertion
There is no need to check every sentence immediately. Find the fact upon which the entire conclusion depends.
For example: "To submit an application, document X is absolutely required." You should start with this requirement. If it is incorrect, the explanations to it are already irrelevant.
Separate Facts from Conclusions
A date, rule, organization name, and technical parameter are verifiable facts. The phrase "therefore, you are likely not to be denied" is already a conclusion.
These should be checked separately. The original fact may be erroneous, and the conclusion may be overly categorical even with correct data.
Request the Source and Degree of Confidence
You can ask the neural network: "What data is your answer based on? Which assertions can't you confirm?"
This does not prove the reliability of the information but helps to identify weaknesses. If the model provides a link, the name of a document or research, check whether such a source exists and whether it indeed contains the claimed information. The mere presence of a link does not confirm anything yet.
Compare the Key Fact with an Independent Source
Requirements for public and commercial services are best checked on the official pages of agencies, organizations, and services. Medical information should be verified according to recommendations from specialized institutions and specialists. Technical instructions should be in the manufacturer's documentation.
The source should not have been created by the same neural network or merely repeat its response without proper verification.
Determine the Boundaries of the Answer
Ask the AI to list the circumstances under which the recommendation might be incorrect. These could be a different country, age, diagnosis, type of contract, program version, or document validity period.
Many errors arise from missing context that the model unwittingly infers.
A Ready-Made Prompt for Verification
Critically evaluate the following answer.1. Identify all verifiable facts.2. Separate facts from assumptions and conclusions.3. Indicate which statements may be erroneous or depend on context.4. Do not invent sources. If you can't confirm a fact, just say so.5. Create a short list of what needs to be checked in independent and, if possible, official sources.Answer: [insert text]Suppose the neural network claims that for returning a product, "an oral request is always sufficient, and the return period is exactly 30 days." The wording sounds convincing, but it does not specify the jurisdiction, does not clarify the type of product, and mentions the period without any reservations.
This is a conditional example, not a description of existing rules. It shows how a confident tone can mask a lack of important conditions.
Where You Shouldn't Trust AI Without Verification
Medicine. The neural network can explain a term or help formulate questions for a doctor but should not replace diagnostics, prescribe treatment, or assess the urgency of symptoms.
Law. Laws vary depending on the country, region, date, and case circumstances. Even one incorrect detail can change the outcome.
Finance. Before making financial decisions, you need to independently verify commissions, risks, contract conditions, and current regulations. A confident tone does not make a recommendation safe.
News. The model may confuse dates, participants, and events or present old information as new. Messages should be cross-checked with several independent publications.
Technical Instructions. A command safe for one version of a program or device could lead to data loss in another. You should first refer to the official documentation and test changes, if possible, on a copy.
When AI is Really Useful
The neural network handles tasks well when a draft, structure, or processing of provided material is needed. It can explain a complex term in simple words, suggest headlines, summarize text, create checklists, identify contradictions in a document, or help formulate a question for a specialist.
It is more beneficial to view AI as a quick assistant rather than a source of ultimate truth. It saves time at the initial stage, but the decision is made by a human. The higher the cost of an error, the more thoroughly the response needs to be verified.