AI给出的答案看起来很可靠时，应该先检查什么？ / What Should You Check When an AI Answer Looks Reliable?

Judgment in the Age of AI · Article 1

The AI errors most worth watching are rarely obvious nonsense. They are often fluent, well organised and rich in detail. Some even include convincing-looking references.

Confidence of expression is not evidence of factual reliability. A language model generates content that fits the context. When information is missing, it may continue constructing an answer instead of stopping to say that it does not know. Plausible but false output is commonly described as hallucination or confabulation.

First, consider the cost of an error

An occasional mistake in a dinner suggestion, writing exercise or travel idea may be easy to notice and correct.

Answers involving medicine, law, finance, current affairs, academic citations, software security or an important professional decision require more care. The more serious the consequences, the stronger the verification should be.

Second, isolate the checkable claims

Do not try to verify the whole answer at once. Mark the elements that can be confirmed or disproved:

• names, dates and numbers
• laws, policies and product versions
• books, papers and quotations
• statements beginning with “research shows” or “experts agree”
• facts on which the final conclusion depends

Opinions may be discussed. Factual claims must be checked.

Third, open the cited source

A listed source may not exist. Even when it exists, it may not support the claim being made.

Check at least four things:

1. Who is the author or responsible organisation?
2. Does the document actually exist?
3. Are its date, jurisdiction and version relevant?
4. Does the original text directly support the conclusion?

A title or search-result summary is rarely enough.

Fourth, verify independently

Do not rely only on asking the same AI to check its answer again. It may repeat the same mistake in different words.

For important questions, return to government websites, original research, official documentation, legislation or a trusted database. When the consequences are significant, look for an independent second source.

A five-question checklist

Before acting on an AI answer, ask:

• What is the most important factual claim?
• Have I opened the original source?
• Could the information be out of date?
• Does it apply to my country and circumstances?
• What could happen if it is wrong?

If these questions cannot be answered, treat the AI output as a lead rather than a conclusion.

AI can help organise a question, identify possible directions and create an initial structure. Its reliability, however, should come from a process that allows checking, correction and traceability—not from the confidence of its language.

References

• NIST: Artificial Intelligence Risk Management Framework—Generative Artificial Intelligence Profile
• OpenAI: Why Language Models Hallucinate