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1. Turn the answer into claims you can check
Read the answer once for its overall point, then separate it into statements that could be verified. Mark factual claims, numbers, dates, claims about cause and effect, and recommendations. A long paragraph may mix all of these together.
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Prioritize claims that matter to your decision. Flag details that can change over time or depend on jurisdiction, population, or other context. A minor descriptive detail and a claim that could affect a health, legal, financial, or safety decision do not deserve the same review effort.
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2. Follow the evidence behind material claims
If the answer cites sources, open them. A citation is a lead to evidence, not proof that the claim is correct. Check that the source exists, that it supports the specific sentence attached to it, and that its date, geography, population, and scope fit your question. Prefer primary or authoritative material when it is available.
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If a claim has no source, look for one independently rather than relying on the AI’s summary. A source can be reputable and still be too old, cover a different jurisdiction, or describe a narrower group than the answer implies. NIST’s AI Risk Management Framework is a voluntary framework for incorporating trustworthiness considerations into the design, development, use, and evaluation of AI systems; its principles support evaluating claims in context rather than accepting them at face value (NIST AI Risk Management Framework).
3. Check current details and uncertainty
Recheck update-sensitive information—such as rules, dates, product specifications, and current procedures—against an authoritative source that is current for the place and situation you care about. Notice whether the answer distinguishes established facts from estimates, assumptions, or uncertainty. An answer that states a changing detail without a date or scope may be misleading even if it was once accurate.
4. Look for bias in framing, not just in facts
An answer can use accurate individual facts and still give a distorted picture by leaving out relevant people, experiences, or alternatives. Ask:
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- Does it treat one group’s experience as universal, or generalize from a limited population?
- Which assumptions are presented as neutral or inevitable?
- Could the wording or recommendation affect different groups differently in the context where you plan to use it?
NIST identifies systemic, computational or statistical, and human-cognitive sources of bias. That means checking only the training data or algorithm misses institutional context and human decisions that can shape how an AI system is built and used (NIST AI RMF Playbook; NIST, Towards a Standard for Identifying and Managing Bias in Artificial Intelligence). Consider who may be affected and how the answer will be used; there is no context-free bias check.
5. Match the review to the consequences
For low-stakes questions, checking a few key claims against dependable sources may be enough. For decisions with substantial consequences, do not act on an AI answer alone: have a qualified person review both the evidence and the conclusion. Keep a record of the claims checked, sources consulted, unresolved uncertainty, and who reviewed the answer when an audit trail matters.
NIST cautions that accuracy measures alone cannot determine whether an AI use is appropriate; potential harms and the intended setting matter. Its guidance supports representative testing, clear evaluation methods, disaggregated results where relevant, ongoing monitoring, and human intervention when a system cannot identify or correct errors (NIST AI RMF Playbook). UNESCO’s Recommendation on the Ethics of Artificial Intelligence also emphasizes transparency, fairness, and human oversight (UNESCO Recommendation on the Ethics of Artificial Intelligence).
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.6. Compare answers or systems on the dimensions that matter
If you are comparing multiple AI answers or systems, do not rely on a single overall score. Compare them on explicit dimensions relevant to your use:
- Factual validity: Are the material claims supported by evidence?
- Source quality: Are sources authoritative, relevant, and correctly represented?
- Coverage: Does the answer include the perspectives and conditions needed for this question?
- Performance in context: Does it work across the populations, situations, or input types that matter?
- Consequence of errors: What could happen if a wrong answer is accepted?
NIST recommends representative evaluation and considering results broken out across relevant groups or conditions; an average can conceal uneven performance. These comparison dimensions are a practical checklist, not a universal NIST scoring rubric (NIST AI RMF Playbook).
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Why detectors and benchmark scores cannot settle the question
An AI-text detector does not tell you whether the statements in an answer are true, and a benchmark score does not certify that an answer is fair or reliable for your particular use. In a NIST text-summarization pilot, summaries from three generators fooled every detector tested. That is a result from that specific pilot, not evidence that every detector always fails (NIST Generative AI program).
NIST’s guidance is to evaluate systems for their intended context and the risks involved; the cited sources do not establish a universal pass score for accuracy or bias. Treat automated scores as limited evidence, then verify the claims and assess who could be affected.
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