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Stop relying on the output, prevent any immediate harm, verify the underlying claims with authoritative evidence or qualified human expertise, preserve a privacy-conscious incident record, and request human review through the provider or deploying organization. If the result affected a consequential decision, contact the person or team accountable for that decision and ask what correction or appeal process applies.
First, contain the risk
A fluent answer is not proof of accuracy. NIST’s Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile (AI 600-1, published July 26, 2024) calls a confident presentation of erroneous or false content “confabulation.” The error may be a wrong fact, a fabricated citation, a contradiction of the system’s earlier answer, or a response that diverges from the prompt.
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Take the following actions before forwarding the answer, publishing it, approving a transaction, or using it in a decision:
- Pause the affected action. Defer an automated approval, denial, diagnosis, payment, disclosure, enforcement step, or other consequential action while the concern is checked.
- Alert the responsible human. Tell the decision owner, supervisor, safety contact, caseworker, clinician, administrator, or service team what happened.
- Prevent further distribution. Mark the result as unverified and avoid copying it into records or sending it to people who might treat it as authoritative.
- Protect people from immediate danger. If someone faces an urgent physical, medical, security, or financial threat, use the appropriate emergency or professional service rather than waiting for an AI provider’s response.
Pausing reliance is a practical risk-control measure derived from NIST’s discussion of consequential confabulation and incident response. It is not a universal statutory instruction, and the correct action depends on the potential severity and the authority you have to intervene.
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Decide whether the result is merely wrong or actively harmful
Separate factual reliability from potential impact. A harmless typo and a false instruction that changes someone’s access to healthcare require different responses.
| Question | What to assess | Why it changes the response |
|---|---|---|
| What could happen if someone follows or shares it? | Physical, medical, discriminatory, legal, financial, privacy, security, reputational, or operational consequences | Higher-consequence outcomes justify faster containment and escalation |
| Who could be affected? | The person who asked, a customer, employee, patient, student, applicant, resident, or a wider community | Broader exposure may require notification and coordinated review |
| Can the action be undone? | Whether a record, payment, access decision, publication, or model-triggered action is reversible | Irreversible actions should be paused earlier |
| How strong is the evidence? | Whether primary documents, authoritative databases, logs, or expert review confirm the error | Weak evidence calls for careful verification; strong evidence supports immediate correction |
| Who can intervene? | The user, a human decision owner, a service administrator, a provider, or a regulator under the applicable process | Escalate to someone with authority to override or stop the workflow |
| What privacy exposure would a record create? | Personal data, health information, confidential business material, credentials, or protected case details | Preserve enough evidence to investigate without creating a second incident |
Do not infer that a confident explanation is reliable. Generative systems can produce persuasive reasoning and citations that are themselves fabricated. Check the claims and the cited material independently.
Verify the claim with evidence that can outrank the model
Check primary and authoritative sources
Use the original regulation, court filing, medical guideline, product manual, official dataset, institutional record, or other source that actually establishes the fact. Search for the document independently instead of clicking a citation supplied by the model. Confirm the title, author, date, section, and wording.
Use qualified human review for specialized or high-stakes questions
A subject-matter expert can identify a subtle clinical, legal, engineering, accessibility, cultural, or security error that a general fact check misses. Tell the reviewer exactly what output was used, what decision it influenced, and what evidence you have already checked.
Test the whole answer, not just one sentence
Check whether the response followed the prompt, whether separate parts contradict one another, whether calculations can be reproduced, and whether assumptions were left unstated. A correct-looking conclusion built on a false premise remains unsafe.
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Assess harm even when the facts are technically correct
An answer can be factually accurate yet harmful because it exposes private information, gives dangerous instructions, reinforces a discriminatory pattern, or is used outside the context in which it is valid. Review the likely use and audience, not only the wording.
Preserve a useful, privacy-conscious incident record
A provider or internal investigator cannot reproduce an issue from “the AI was wrong” alone. Save enough context to establish what the system received, what it produced, how it was used, and what went wrong.
- The exact prompt, input data, uploaded files, system instructions, or workflow step that shaped the result
- The complete output, including warnings, citations, tool results, and visible confidence indicators
- The product name and model or version, if displayed; also record relevant configuration, retrieval source, plug-in, or automation setting
- The date and time, time zone, user or service account, and the intended purpose
- The person or process that relied on the result and the action taken or proposed
- The suspected factual error, harmful content, affected people, and potential or actual impact
- Independent evidence showing the correction, such as an official document or expert assessment
- Copies of reports, case numbers, responses, and decisions about correction or escalation
Minimize what you retain. Remove unnecessary names, identification numbers, credentials, health details, customer records, and confidential material before sending a report, unless the authorized investigator specifically needs them. Follow your organization’s retention, access-control, and privacy rules. A screenshot may help establish appearance, but preserve machine-readable text or logs when they are available and lawful to keep.
Report the output and ask for a specific remedy
Use the channel that can change the system or decision
Submit the issue through the provider’s support, safety, feedback, or grievance process. If the system is embedded in another service, report it to the deploying organization as well. When an AI output affected a real-world decision, notify the human decision owner or responsible service team instead of relying only on a generic product feedback button.
Write a report that an investigator can act on
- Identify the event. State the product or internal system, approximate time, model or version if known, and the workflow involved.
- Describe the failure precisely. Quote the relevant output and explain whether it was false, inconsistent, unsafe, discriminatory, privacy-invasive, or misleading.
- Attach independent evidence. Provide the authoritative source, reproducible calculation, expert assessment, or other basis for the correction.
- Explain the impact. Say who was affected, what action was taken or blocked, whether the harm is reversible, and whether the result was shared further.
- Request an outcome. Ask for correction, deletion or amendment of an incorrect record, human review, an appeal, an override, withdrawal of the output, or confirmation that use has been restricted.
- Protect the case. Keep the submission, attachments, date, recipient, and case reference. Do not include sensitive data that the recipient does not need.
Ask when you should expect an acknowledgement and how to provide additional evidence, but do not assume one universal deadline. The cross-sector NIST materials do not establish a single reporting authority, response clock, or remedy for every AI incident.
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How to obtain human review or appeal a decision
When an AI result influenced employment, credit, education, healthcare, housing, public services, benefits, security, or identity, ask the organization:
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- What information did the system provide, and what other evidence was considered?
- How can you submit a correction or supporting documents?
- Can a qualified person review and override the result before it takes effect?
- What appeal, grievance, reconsideration, or complaint route applies to this service?
- How will the organization correct downstream records or notify other affected parties?
Routes and rights vary by sector and jurisdiction. NIST’s cross-sector guidance does not replace applicable law, contract terms, professional obligations, or an organization’s published appeal policy.
What organizations should do after a harmful output
NIST’s AI Risk Management Framework (AI RMF) Core assigns organizational work beyond collecting user complaints. The organization should treat the report as an incident or near miss, determine scope, and create a documented response.
1. Triage severity and scope
Identify the affected model, deployment, data source, prompt or workflow, users, and decisions. Determine whether the problem is isolated or reproducible, whether other outputs may be affected, and whether the incident is ongoing. Prioritize cases with severe potential harm, many affected people, irreversible consequences, or active exposure.
2. Stabilize the workflow
Stop or limit the risky feature, add a human checkpoint, remove a faulty retrieval source, disable an integration, or revert to a known-safe process. If the system’s performance or outcomes no longer match intended use, the AI RMF calls for mechanisms to supersede, disengage, or deactivate it.
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3. Investigate the system and its context
Review prompts, model and data versions, retrieval results, filters, tool calls, user permissions, interface warnings, human decisions, and monitoring alerts. Test whether a change in wording, language, demographic context, document type, or operating condition reproduces the failure. Investigate both the model and the surrounding deployment; a harmful outcome may arise from workflow design even when the model output appears ordinary.
4. Correct the immediate impact
Provide a human review or override, correct inaccurate records, reverse an eligible action, notify affected people, and offer a clear route to submit additional evidence. Tell recipients what was wrong and what has changed without exposing another person’s private information.
5. Communicate with relevant actors
The AI RMF calls for communicating incidents and errors to relevant actors, including affected communities. Tailor the notice to the audience: an operator may need technical indicators, while an affected person needs the decision status, correction route, and contact for human assistance.
6. Monitor whether the fix works
Define a measurable check for recurrence, review new user feedback, and examine whether the intervention created new failure modes. Keep the incident, decision, evidence, and follow-up results in a record that authorized staff can audit.
7. Update controls and ownership
Revise instructions, evaluation sets, access rules, human-review thresholds, training, documentation, and escalation contacts. Assign an owner for each corrective action and a date for verification. Add a process for previously unknown risks instead of assuming the original assessment was complete.
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Human support requirements in digital identity systems
NIST SP 800-63-4 contains more prescriptive provisions for the specific context of digital identity. It says relying parties and credential service providers must make human support personnel available to intervene in and override issue-adjudication outputs from algorithmic support mechanisms. It also calls for educating support staff about issue handling and redress, and for processes to report and address end-user barriers and grievances.
Those provisions should not be presented as a universal rule for every AI product. They illustrate what a concrete human-override and grievance design can look like in a defined sector. An identity service should publish how a person reaches support, what evidence is accepted, how an account or credential decision can be reconsidered, and how staff record and resolve barriers.
Build prevention into an AI deployment
Reporting is more effective when the system already has an accountable operating model. Before deployment, document:
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- The intended uses, prohibited uses, affected groups, and decisions that require human authority
- Who monitors performance, receives complaints, approves changes, and can stop the system
- What logs are collected, how long they are retained, who can access them, and how sensitive data is protected
- What evidence triggers a human review, override, rollback, restriction, or shutdown
- How users are told that an AI system is involved and how they report an error or adverse outcome
- How corrections reach downstream systems, records, customers, and communities
- How post-deployment monitoring, user feedback, red-team findings, and incident trends change the controls
NIST describes transparency as often necessary for actionable redress when AI outputs are incorrect or cause negative impacts. Transparency does not by itself guarantee accuracy or fairness; it must be connected to a person or team that can investigate and act.
What NIST’s current guidance does—and does not—establish
The AI RMF is voluntary, cross-sector guidance, and NIST says it is being updated. Its stable themes include accountable human oversight, monitoring after deployment, user and stakeholder input, appeal and override, incident response, recovery, communication, and the ability to disengage or deactivate systems that are not performing as intended.
The companion AI 600-1 profile describes 13 generative-AI risks and more than 400 associated actions. NIST says a public working group of about 2,500 participants contributed to its development. These are characteristics of the profile and its development, not measurements of how often harmful outputs occur or how effective a reporting channel is.
The cited NIST materials do not provide a general prevalence rate for harmful or incorrect AI outputs, a universal regulator, one reporting deadline, or one guaranteed remedy. Apply the process required by the relevant service, sector, contract, and jurisdiction.
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Common mistakes that make incidents worse
- Trusting tone or detail. A long explanation, confident wording, or realistic citation can still be fabricated.
- Correcting only the visible sentence. A hidden prompt, retrieval source, or downstream automation may continue producing the same error.
- Deleting the only evidence. Preserve a minimal authorized record before changing the workflow, while avoiding unnecessary personal data.
- Posting sensitive material publicly. A public warning can expose the very person the response is meant to protect.
- Reporting without asking for a remedy. A complaint that does not identify the requested correction, review, or override is harder to resolve.
- Assuming a generic support response is human review. Ask who examined the decision and whether that person had authority to change it.
- Leaving a disabled feature undocumented. Record why it was restricted, who approved the change, and what evidence is required before reactivation.
A compact response checklist
- Pause reliance on the output and contain foreseeable harm.
- Verify the claims and citations independently.
- Obtain qualified human review when the subject or stakes require it.
- Record the prompt, output, system context, time, impact, and supporting evidence.
- Redact unnecessary personal or confidential information.
- Report the issue to the provider, deployer, and accountable decision owner.
- Request a correction, human review, appeal, override, or other specific remedy.
- Escalate urgent or consequential cases through the applicable sector process.
- Keep the case reference and monitor whether the correction works.
- If you operate the system, restrict, supersede, disengage, or deactivate it when outcomes no longer match its intended use.
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