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Stop the affected action, verify the output against authoritative evidence, preserve a privacy-conscious record, and request human review. A fluent answer can still be false or internally inconsistent. If someone could be injured, discriminated against, denied a benefit, exposed financially, or have private information revealed, treat the output as an untrusted signal until a qualified person checks it.
Why a confident AI answer can be wrong
NIST AI 600-1 calls this failure mode confabulation: a generative AI system confidently presents erroneous or false content, including content that diverges from the prompt or contradicts its earlier output. Statistical language generation can produce accurate prose and still invent facts, sources, reasoning, or citations. Open-ended and domain-specific tasks are particularly difficult, and a persuasive explanation is not evidence that the underlying claim is true.
Confabulation matters most when people act on the result. An incorrect medical suggestion, eligibility determination, legal explanation, safety instruction, or financial calculation can create consequences well beyond the chat session. NIST also notes that the extent to which people can be deceived by language models and the downstream scale of these impacts are difficult to estimate; there is no reliable general incident-rate figure to use as a safety assumption.
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- Is the output accurate? Check each material claim, not merely the conclusion or tone.
- Could following or sharing it cause harm? A factually correct answer can still be unsafe in a particular context, while a small factual error can become serious when it controls a high-stakes decision.
What to do immediately
The following sequence is a practical risk-control approach. It is not a universal statutory procedure; legal duties, deadlines, and remedies depend on the sector, organization, and jurisdiction.
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1. Pause reliance and contain foreseeable harm
Do not treat the output as authoritative while it is being checked. If possible, halt, defer, or reverse the action that depends on it. Tell the person who owns the decision or workflow that the result is in question. For an unsafe instruction, stop the activity and use an established safety procedure. For a suspected privacy exposure, avoid forwarding the content and limit access to people who need it for investigation.
Containment can include disabling an automated handoff, placing a case on manual review, withholding a notification, or preventing a generated message from being sent. Choose the least disruptive action that prevents additional harm, but favor a pause when the possible consequence is severe, hard to reverse, or affects many people.
2. Verify the underlying claims
Use primary documents, authoritative databases, the original source material, or a qualified domain expert. Inspect every generated citation independently: a plausible title, link, quotation, or legal reference can be fabricated. Compare dates, definitions, units, geographic scope, and version numbers. If the answer contains calculations, recompute them with a trusted method.
For a high-impact decision, obtain review from a person who has authority and relevant expertise, rather than asking another unqualified model to vote on the answer. Record which claims were confirmed, disproved, or left unresolved. If evidence conflicts, preserve the uncertainty instead of selecting the most confident-sounding response.
3. Preserve a useful, privacy-conscious record
Save enough context for another person to reproduce and investigate the issue, while collecting no more personal or confidential information than necessary. A practical incident record includes:
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- the complete output, including warnings, citations, and attached files;
- the prompt, user inputs, retrieved documents, or other context that materially shaped the result;
- the product, model, and visible version or configuration;
- the date and time, intended use, and workflow or decision involved;
- who relied on the result and which people or groups may have been affected;
- the suspected error, harmful instruction, discriminatory effect, privacy exposure, or other impact;
- evidence used to verify the problem and the status of any correction or appeal.
Redact unnecessary names, account numbers, health details, credentials, trade secrets, and other sensitive data. Store the record under the organization’s retention, access-control, and privacy rules. This checklist is a practical investigation aid, not a universally mandated reporting schema.
4. Report the result and request a specific remedy
Use the provider’s or deployer’s available support, feedback, safety, incident, or grievance channel. Send a concise description of what the system produced, why it is wrong or harmful, the evidence supporting that assessment, who was affected, and what you want done. A request might be correction of a record, removal of an unsafe output from a workflow, a human review, an appeal, or confirmation that use has been paused.
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If the AI result influenced an organizational decision, report it to the human decision owner or responsible service team as well as the technical provider. Keep a copy of your submission, the date sent, and any case or ticket number. Ask who is accountable for the decision and how you will be notified of the outcome.
5. Escalate in proportion to the risk
Urgent physical-safety concerns should go immediately to the responsible organization and the appropriate emergency or sector process. For employment, credit, education, health, public-service, or identity decisions, ask the organization which human-review and appeal route applies. Do not assume that a vendor’s chatbot, a generic feedback button, or a regulator is the correct first destination.
The cross-sector NIST material does not establish one reporting authority or deadline for every AI incident. Confirm the applicable route for your location and the decision involved, and keep evidence of each escalation.
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6. Close the loop
The responsible organization should determine the scope and severity, communicate with affected people and relevant teams, investigate both the model and the deployment context, correct the immediate issue where possible, and monitor whether the correction works. Depending on the risk and the organization’s authority, an appropriate response can include correcting information, providing a human override, changing a workflow, restricting a feature, superseding a model, disengaging an automation, or deactivating the system.
Document the decision, its owner, the evidence considered, and the follow-up check. An incident is not closed merely because a ticket was answered; closure requires a reasoned determination that the risk is controlled or that a further action is assigned.
How to judge urgency and choose a response
Before deciding whether to pause a whole service or handle one output manually, weigh the factors below together:
- Severity: Could the result cause injury, discrimination, financial loss, legal prejudice, privacy harm, or denial of an essential service?
- Likelihood: Is the problem a one-off mistake, or does the same prompt, data source, or configuration reproduce it?
- Reversibility: Can the consequence be undone, or will a notice, disclosure, payment, or denial have lasting effects?
- Reach: How many people, cases, or downstream systems may have received the result?
- Evidence confidence: How strong and independent is the proof that the output is wrong or harmful?
- Privacy exposure: Would retaining or sharing the record create additional risk?
- Ability to intervene: Can a responsible human stop, correct, or override the outcome now?
A severe, hard-to-reverse issue with broad reach warrants immediate containment and accountable human control, even when some facts remain uncertain. A low-impact, readily reversible error may be logged, corrected, and monitored without disabling an entire system. The rationale belongs in the incident record.
Getting a real human review or appeal
Use plain language and identify the decision, not only the model. State that an automated or AI-assisted result may be incorrect, explain the practical consequence, and ask for the person or team authorized to review and override it. Request the records needed to understand the decision where the organization is allowed to provide them.
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In an employment matter, contact the human decision owner or designated people-operations process. For credit or financial eligibility, ask the responsible institution for its established reconsideration or dispute route. In education, health, and public services, use the institution’s complaint, safeguarding, patient, student, or case-review process as applicable. For identity systems, ask the relying party or credential provider for human assistance and an appeal path rather than relying solely on an automated support mechanism.
Keep the request factual: identify the output, provide corroborating evidence, describe the impact, and specify the correction or review sought. Avoid submitting unnecessary sensitive information through an unverified channel.
What organizations should build before an incident
Assign ownership and oversight
Define who can approve a use, monitor it after deployment, pause it, override a result, communicate with affected people, and authorize recovery. Human oversight must be operational: staff need authority, time, training, and an accessible escalation path. A person who can see an error but cannot stop the workflow is not an effective control.
Make reporting and redress findable
Provide a visible support, safety, or grievance route, explain what information to include, and tell users how a possible adverse outcome will be reviewed. NIST’s AI Risk Management Framework says transparency can be necessary for actionable redress when outputs are incorrect or cause negative impacts. Transparency should include how a human operator or user is notified when a potential or actual adverse outcome is detected; it does not by itself guarantee accuracy or fairness.
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Monitor after deployment
Collect and evaluate input from users, operators, affected communities, and other relevant actors. Look for recurring errors, internal contradictions, fabricated citations, unsafe instructions, disparate effects, privacy incidents, and failures introduced by changes in data or workflow. Track incidents and near misses, not just model-quality scores. Establish a process for previously unknown risks so that a new failure mode can trigger review rather than waiting for a scheduled update.
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Prepare response and recovery procedures
Write down how to triage, preserve evidence, notify affected actors, investigate, correct records, provide human review, and test a fix. Include criteria for restricting, superseding, disengaging, or deactivating a system when performance or outcomes no longer match the intended use. After recovery, verify that the change works in the real workflow and record lessons for monitoring and controls.
Communicate with affected people
Explain what happened, what is known and uncertain, what action the person can take, and when the next update will arrive. Do not imply that a model’s confidence score proves correctness. If the organization cannot disclose a technical detail, it can still identify the decision owner, the available remedy, and the evidence needed for review.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Where NIST guidance fits
The NIST AI Risk Management Framework is voluntary, cross-sector guidance. NIST says the framework is being updated, so its durable principles should be distinguished from the status of any particular version. The companion Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile (NIST AI 600-1) was published on July 26, 2024.
| Item | Figure | Meaning |
|---|---|---|
| Risks covered by NIST AI 600-1 | 13 | A description of the profile’s risk coverage, not a count of incidents. |
| Actions described by NIST AI 600-1 | More than 400 | Recommended actions in the profile, not a measured success rate. |
| Public working-group participants | 2,500 | People who contributed to development, not people affected by harmful outputs. |
The AI RMF Core assigns responsibilities for identifying incidents and sharing information, defining human–AI oversight, monitoring after deployment, collecting user input, providing appeal and override, documenting response and recovery, communicating incidents and errors to relevant actors, and tracking, responding to, and recovering from incidents. It also calls for mechanisms to supersede, disengage, or deactivate systems whose performance or outcomes do not match intended use.
NIST SP 800-63-4 is more prescriptive, but its human-support and grievance provisions apply to digital identity systems. In that context, relying parties and credential service providers must make human support personnel available to intervene and override issue-adjudication outputs from algorithmic support mechanisms, educate support staff on issue handling and redress, and implement processes to report and address end-user barriers and grievances. Those provisions should not be presented as a universal requirement for every AI product or sector.
Common mistakes that make an incident worse
- Arguing from tone: Fluency, confidence, a detailed chain of reasoning, or a citation format does not establish truth.
- Checking only the final sentence: An answer can contain one accurate conclusion built on fabricated premises.
- Asking another model to be the sole judge: Independent evidence and qualified human review are stronger controls for consequential decisions.
- Sharing the full transcript publicly: This can expose personal data, confidential material, or instructions that cause further harm.
- Deleting the record immediately: Removal may prevent investigation; preserve a controlled copy and redact unnecessary data instead.
- Reporting without a requested remedy: A provider may acknowledge a complaint without correcting the decision. State whether you seek a correction, human review, appeal, withdrawal, or safety restriction.
- Assuming one universal deadline or regulator: Routes vary by jurisdiction, sector, and the organization that made the decision.
- Closing the ticket after a model change: Verify the fix in the affected workflow and watch for recurrence.
A concise incident-report template
Use the following headings in a support or grievance submission, adapting them to the channel’s privacy limits:
Quick Recap
- Decision or workflow: identify what the AI result was used to do.
- Date, product, and version: include the visible model or configuration.
- Output and input context: attach the relevant text or files and the prompt when safe to do so.
- Problem: state the false claim, unsafe instruction, discriminatory effect, privacy issue, or other concern.
- Evidence: cite the authoritative document, database, expert assessment, or observed impact that supports the concern.
- People affected: describe the scope without unnecessary personal details.
- Action already taken: note any pause, correction, notification, or containment.
- Requested outcome: specify the human review, appeal, correction, withdrawal, or system restriction you seek.
- Contact and follow-up: provide a safe contact method and ask for a case reference and response owner.
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