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Stop before acting on it. Treat a fluent AI response as an unverified suggestion, not as evidence or professional advice. Check the claims independently, judge the possible harm, protect private information, report the specific problem through the service’s available channel, and obtain qualified human review whenever safety, money, legal status, employment, education, health, or other rights are involved.
The right next step depends on the stakes. A wrong tone in a casual answer calls for correction and feedback; dangerous instructions call for immediate human or emergency help; an institutional decision affecting your rights calls for an explanation and a review route.
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First, decide whether anyone could be harmed
Do not continue experimenting with an answer until you know what could happen if it is wrong. Separate the response’s factual claims, assumptions about you or other people, and recommendations for action. A confident style is not proof of accuracy. The National Institute of Standards and Technology (NIST) treats validity and reliability, safety, accountability, and transparency as different characteristics of trustworthy AI, so a response can sound convincing while failing one or more of them.
Use this severity triage
| Situation | What to do now | Why |
|---|---|---|
| Low-stakes factual error, awkward tone, or missing context | Do not repeat the claim as fact. Check a reliable source, ask the tool to clarify if useful, and submit precise feedback. | The likely harm is limited, but leaving the error unchallenged can spread it. |
| Potentially harmful health, safety, financial, security, or legal advice | Stop and consult an appropriately qualified professional or authoritative service before taking action. | The cost of a wrong recommendation can be serious even when the wording appears cautious. |
| An organization used AI in a decision about your safety or rights | Ask which person or team is accountable, request an explanation, and ask for human review or correction. | People need a route to challenge consequential decisions. |
| Immediate danger or a risk of serious injury or death | Use emergency services or the appropriate urgent specialist resource. Do not wait for the AI provider to respond. | NIST says risk responses should match context and severity, with serious injury or death risks receiving the most urgent priority. |
NIST’s AI Risk Management Framework is voluntary guidance, not a consumer complaint procedure. Its principles are still useful for deciding how much caution a particular output requires.
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Do not let the answer become its own evidence
For anything consequential, verify the answer outside the tool. The Federal Trade Commission (FTC) has warned, in a report about AI used to address online harms, that such systems can be inaccurate, classify material incorrectly, miss new phenomena, and lack context or meaning. That report does not establish an error rate for all generative-AI answers, but it supports a basic rule: an AI output cannot prove the claim it makes.
A practical verification method
- Extract the claims. Write each checkable statement separately. Distinguish a date, number, diagnosis, quotation, legal conclusion, or prediction from the tool’s general commentary.
- Find a primary or authoritative source. For a regulation, use the relevant government or court publication; for medical guidance, use a qualified clinician or public-health authority; for a product or account rule, use the provider’s current documentation.
- Check the conditions. Confirm the country, edition, date, age group, account type, or other assumptions. A statement can be accurate in one setting and unsafe in another.
- Get an independent human judgment when the consequence is material. Ask a professional to review the underlying facts, not merely whether the AI’s prose sounds reasonable.
- Record what changed. Keep the verified correction and the original wording separate so later readers do not mistake the correction for the tool’s answer.
Ask the tool for sources only as a lead. Check those sources yourself, because a citation can be incomplete, misquoted, or unrelated to the conclusion.
Recognize what “biased” can mean
A response may be biased when it applies a stereotype, makes an unsupported group-based assumption, omits relevant context or perspectives, or treats comparable people differently. A single offensive or unfair answer is evidence of a harmful output, but it does not by itself establish how the entire system behaves in every situation.
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NIST’s publication Toward a Standard for Identifying and Managing Bias in Artificial Intelligence (SP 1270, published March 15, 2022) describes three broad sources:
- Systemic bias: patterns in institutions, policies, or social conditions that shape data and outcomes.
- Computational and statistical bias: problems in sampling, labels, measurements, objectives, or model performance.
- Human-cognitive bias: assumptions and judgment shortcuts introduced by people who create, deploy, evaluate, or use a system.
These categories matter because harmful results can occur without deliberate prejudice. Focus your report on the observable behavior and its effect rather than claiming to know the developer’s intent.
Describe the problem concretely
- Quote the exact passage that is inaccurate, stereotyping, exclusionary, or dangerous.
- Explain which relevant fact, perspective, or limitation was omitted.
- Describe how the answer treats people or cases differently, if that is the issue.
- State the foreseeable harm: for example, denial of an opportunity, unsafe action, exposure of private information, or escalation of harassment.
- Provide the context needed to reproduce the problem, without including unnecessary personal data.
Calling an answer “biased” because it disagrees with you is not enough. Test whether the response relies on an unsupported assumption or produces an unfair effect in the circumstances that matter.
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Protect sensitive information while checking or reporting
Preserve enough of the interaction to describe it accurately, but do not copy secrets into a report or a new chat. Remove passwords, authentication codes, payment details, health records, government identifiers, confidential work material, and another person’s private information unless disclosure is necessary, lawful, and safe.
UNESCO’s Recommendation on the Ethics of Artificial Intelligence, adopted in November 2021, identifies privacy and data protection as protections that should apply throughout the AI lifecycle. That principle does not tell you what a particular service retains or shares. Check the provider’s current privacy and help documentation before assuming that deleting a chat removes every copy or that a report is private.
Make a safe record
- Save the date and time, the tool or feature name, and the relevant conversation or decision reference.
- Capture the prompt and answer only after redacting sensitive content.
- Note whether the answer was generated once or repeated after a fresh session; this can help an investigator reproduce the behavior, but it is not a scientific test of the whole system.
- Keep your original evidence in a secure location and share the minimum necessary version with support staff.
Report the response through the available channel
Services use different labels and workflows. Look for controls such as “thumbs down,” “Report,” “Safety,” “Feedback,” or a support form in the product’s help center. The available interface, escalation route, and data handling vary by provider, so do not assume that one service’s process exists elsewhere.
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What a useful report contains
- Location: identify the conversation, feature, account context, and approximate time.
- Reproduction: include the prompt and the relevant output after removing secrets.
- Issue type: select the closest category, such as discrimination, dangerous advice, privacy, fraud, or misinformation.
- Specific explanation: identify the exact sentence and why it is false, unfair, unsafe, or missing necessary context.
- Impact and urgency: explain who could be harmed and whether the risk is ongoing or immediate.
- Requested action: ask for correction, removal, escalation, or a human response when those options exist.
A concise description is more actionable than a general statement that the system is “bad.” Do not submit personal records merely to make the complaint look serious.
A report template
Feature and date: [name and approximate time]
Prompt: [redacted prompt]
Problematic passage: [exact excerpt]
Issue: [inaccuracy, stereotype, unsafe instruction, privacy issue, or other category]
Why it matters: [specific foreseeable harm]
Context needed to reproduce it: [non-sensitive details]
Requested follow-up: [correction, escalation, or human review]Ask for human review when a decision affects you
If an employer, school, lender, insurer, public agency, platform, or other organization used AI to inform a decision about your safety or rights, ask for the accountable person or team. Request the reason for the decision in understandable terms, the information used, and a way to correct inaccurate data. Ask whether a human can review the result without simply accepting the model’s recommendation.
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Questions to put in writing
- Was an automated system used, and what role did it play in the decision?
- Which facts or records were considered?
- Who is responsible for reviewing an error?
- How can you submit corrections or supporting documents?
- What deadline applies to an appeal or review?
- Will a qualified person reconsider the outcome rather than merely rerun the system?
Keep copies of your request and the response. If the matter involves discrimination, unsafe conditions, loss of essential services, or another protected interest, consider an appropriate regulator, ombuds office, advocate, or lawyer in your jurisdiction.
Choose the response that matches the stakes
| What you saw | Best next step | Avoid |
|---|---|---|
| Rude wording or a minor omission | Ask for a neutral rewrite, verify any factual claim, and report the passage if it reflects a recurring harmful pattern. | Assuming intent or publishing the answer as an authoritative statement. |
| Stereotype or unequal treatment | Document the exact language, explain the omitted context and impact, and request review. | Trying to prove the entire model is biased from one exchange. |
| Medical, legal, financial, or security instruction with meaningful downside | Stop, verify with a qualified professional or authoritative source, and report the unsafe recommendation. | Following a disclaimer-free or confident answer because it sounds specific. |
| AI-assisted institutional decision | Request an explanation, correction channel, and human reconsideration. | Accepting “the computer decided” as a complete explanation. |
| Immediate threat to life or physical safety | Contact emergency or urgent specialist services first; preserve evidence afterward if safe. | Waiting for a feedback queue or continuing to test prompts. |
What not to infer from one bad answer
One interaction can reveal a real harm without revealing its frequency, cause, or scope. Avoid claiming that a whole tool is always biased, that every answer is unsafe, or that a particular percentage of outputs are wrong unless a credible study actually supports that claim. Also avoid treating the tool’s refusal, apology, or revised answer as proof that the underlying problem has been fixed.
Look for the practical outcome: Was a person misclassified? Was a dangerous instruction given? Was private information exposed? Was a decision corrected? Those facts are more useful than speculation about an opaque model’s internal reasoning.
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- Use AI for brainstorming or low-risk drafts only when a person can check the result before it matters.
- Keep consequential decisions with accountable humans who have the authority and expertise to correct errors.
- Give staff a written procedure for verification, escalation, privacy protection, and record keeping.
- Train users to separate confidence, fluency, and personalization from evidence.
- Review whether the tool is appropriate for the population and context, especially where language, disability, culture, age, or access differences may change the effect.
UNESCO links responsible AI practice with education and media and information literacy. In practical terms, teach people how to question an output, find an independent source, recognize missing context, and reach a human decision-maker.
Key principles to remember
- Pause before acting. The more severe the possible harm, the faster you should move to qualified human or emergency help.
- Verify independently. The output is a lead, not its own evidence.
- Describe observable harm. Identify the exact language, missing context, unequal treatment, or unsafe instruction.
- Protect privacy. Redact secrets and check the provider’s current terms rather than guessing about retention.
- Demand accountability for consequential decisions. Ask who can explain, correct, and review the result.
FTC Bureau of Consumer Protection Director Samuel Levine summarized the limitation in a statement about AI used to address harmful online content: “Our report emphasizes that nobody should treat AI as the solution to the spread of harmful online content.” The same caution applies here with the appropriate scope: an AI tool can assist investigation, but it should not replace evidence, professional judgment, or a human route to correction.
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