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If an AI system appears to cause harm or treat people unfairly, first reduce the risk of further harm. If you operate the system, consider pausing or limiting the affected use and routing consequential decisions to qualified human review. Preserve relevant records, alert accountable people, investigate who was affected and why, then correct the workflow and monitor it before resuming. If you are affected by a decision, save the notice and use the organization’s review or appeal channel promptly.
Start with the role you have and the urgency of the harm
The right first step depends on whether you control the system, were affected by its output, or are responsible for evaluating it. It also depends on whether harm is continuing, whether a consequential decision has already been made, and which laws apply. A system can contribute to a harmful outcome even when a person formally made the final decision: the output may have shaped what that person saw, how quickly they acted, or whether they considered alternatives.
| Situation | First priority | Next action |
|---|---|---|
| You operate or deploy the system | Contain the affected use when appropriate, and prevent unchecked reliance on suspect outputs. | Preserve records, notify the operational owner and relevant provider, and begin an impact review. |
| You were affected by an AI-supported decision | Save the decision notice and any explanation or correspondence. | Ask who is responsible for review and how to appeal; submit a timely appeal through a verified channel. |
| You assess or govern the system | Establish who can suspend or restrict use and who owns the response. | Coordinate impact assessment, legal and privacy review, communications, and monitoring. |
Do not assume that an AI system is the only cause. The harm may arise from its data, design, thresholds, instructions, integration into a workflow, or the way people rely on its outputs. Investigate the entire decision process.
Contain ongoing harm before conducting a full investigation
If you control the system or workflow, assess whether the affected feature or use case should be paused, restricted, or routed to a human decision-maker while the concern is examined. Depending on severity and your authority, options may include reverting to a known safer version, narrowing the use, switching to a viable non-AI process, or adding qualified human review. A person reviewing an output should have the information, authority, and time needed to question it—not simply approve it automatically.
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Choose a response proportionate to the risk. A suspected error in a low-impact internal task may call for a limited restriction and review. A pattern affecting access to employment, housing, education, health services, public benefits, or other important opportunities may warrant more urgent action and broader assessment. These examples describe consequential contexts; they do not by themselves determine a system’s legal classification.
NIST’s AI Risk Management Framework (AI RMF) Core treats disengaging or deactivating a system with outcomes inconsistent with intended use as a risk-management response. It also calls for procedures to respond to and recover from previously unknown risks. The framework is voluntary guidance, not a universal legal checklist. Its FAQ describes the aim as helping developers, users, and evaluators manage AI risks that could affect individuals, organizations, society, or the environment.
If you are the person affected
Save the decision, message, or notice, including the date, the organization that issued it, and instructions for review. Ask the organization to identify the responsible decision-maker, explain whether and how an AI output influenced the decision, and provide the review or appeal process. If a deadline is stated, act promptly rather than waiting for a complete explanation. Keep communications factual and retain copies of what you send and receive.
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Use a contact channel you can verify through the organization’s official website or existing account. Do not send sensitive personal information to an unverified address or person. If the decision concerns immediate safety or access to an essential service, use the organization’s urgent or human-support channel as well as any formal appeal route.
Preserve records that let people reconstruct what happened
Document the incident while details are fresh. A useful record allows reviewers to identify the system state, the context in which it was used, the output, the resulting action, and subsequent changes. Preserve records under your ordinary privacy, security, and retention controls; do not create an uncontrolled copy of sensitive data merely to keep evidence.
- Date and time of the output, decision, discovery, and any response.
- System name, provider, version, configuration, and relevant changes, if known.
- The task the system was performing and the actual workflow in which it was used.
- Relevant inputs and outputs, with appropriate protections for personal or confidential information.
- The decision or action taken, who relied on the output, and any human review, override, or appeal.
- Who may have been affected, what harm is suspected, and whether the effect is ongoing.
- Containment and correction steps, who authorized them, and when they took effect.
Preserve the original records where possible, and distinguish contemporaneous records from later analysis. Record uncertainty rather than filling gaps with assumptions. NIST emphasizes tracking, documenting, communicating, and recovering from incidents; the fields above are practical incident-record recommendations, not a mandatory NIST form.
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Escalate to people who can act
Notify the operational owner and the organization’s designated AI risk or incident-response contact. If an external provider is involved, notify the appropriate provider contact with enough detail to investigate, while sharing personal or security-sensitive information only through approved channels. Do not assume that a provider notification alone fulfills the organization’s responsibilities to affected people or regulators.
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Bring in the functions relevant to the type of harm: legal and compliance, privacy, security, safety, employment or discrimination compliance, and communications. Assign an incident lead and make clear who has authority to pause use, approve a workaround, contact affected people, and decide when any resumption can be considered. NIST’s AI RMF Core calls for assigned responsibilities and communication of incidents and errors to relevant AI actors, including affected communities.
For an individual, escalation usually means contacting the organization that made or communicated the decision, then following its stated review process. Depending on the country, sector, and decision, a relevant regulator, rights or employment agency, privacy authority, or qualified lawyer may also be appropriate. No single reporting route applies to every AI-related complaint.
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Investigate the impact, not just the model’s average score
A favorable overall accuracy or performance score does not establish that a system works fairly in the setting where it was used. Examine the people and decisions affected, the conditions of use, and the whole workflow. NIST’s guidance treats trustworthiness as relevant across pre-design, development, deployment, use, testing, and evaluation. Its work on harmful bias highlights identifying, understanding, measuring, managing, and reducing bias as important areas for standards development.
Questions for an impact review
- What was the system intended to do, and what did it actually do in this workflow?
- Which decision relied on the output, and what practical consequences followed?
- Who may have been affected, including groups that could be obscured by an overall average?
- Were inputs, operating conditions, or user populations different from those used during evaluation?
- Could a person challenge the output or obtain meaningful human review before or after the decision?
- Were other options available, and did staff treat the output as a recommendation or as a final answer?
- Did a change to data, configuration, thresholds, software, or the surrounding process precede the result?
Test under representative conditions for the actual use and affected groups. Examine inputs and data quality, design choices, thresholds, provider changes, the surrounding workflow, and human reliance. Where legally and ethically appropriate, compare outcomes across relevant groups and investigate whether observed differences reflect harmful impact. Protect sensitive data and avoid collecting or inferring protected characteristics without an appropriate basis. The cited NIST materials do not prescribe one fairness metric that fits every system; the right evaluation depends on the decision, the context, and the harm being assessed.
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Correct the cause and verify the response before resuming
A model change alone may not fix a harmful outcome. The problem could be an unsuitable use, poor inputs, an unrealistic threshold, inadequate human review, or an appeal process that does not work. Possible treatments include disabling the feature, changing the workflow, improving review and appeal routes, changing data or thresholds, switching systems, or ending the use. Choose a response that addresses the identified cause and the consequences for affected people.
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- Define the correction. State what changed, why it should address the cause, and which decisions or users it affects.
- Assign accountability. Name who owns implementation, residual-risk acceptance, communications, and continuing monitoring.
- Test the changed process. Evaluate it in representative conditions, including the groups and situations implicated in the incident.
- Set conditions for any return to use. Specify what evidence is needed, who approves resumption, and what would trigger another pause.
- Monitor for recurrence. Review outcomes, user feedback, appeals, overrides, and errors, and document response and recovery actions.
Do not treat an unverified change as proof that the problem is resolved. NIST’s post-deployment guidance includes monitoring, appeals and overrides, incident response and recovery, change management, and decommissioning. It also calls for communicating incidents and errors to relevant AI actors, including affected communities. Whether a particular communication is legally required depends on the applicable rules and circumstances.
Understand the legal boundary before relying on a deadline
Legal obligations depend on jurisdiction, sector, the organization’s role, the decision, and whether a system falls into a regulated category. The AI RMF is voluntary guidance; it does not create one U.S. or global reporting rule for every harmful or discriminatory result. For a consequential decision or continuing harm, identify the relevant sector regulator, rights or employment agency, privacy authority, and qualified counsel as appropriate.
EU example: specific duties for certain high-risk systems
The consolidated EU AI Act text identified as version 02024R1689-20260727 describes duties for deployers of high-risk AI systems. A deployer that has reason to consider that use in accordance with the instructions may present a risk described in Article 79(1) must, without undue delay, inform the provider or distributor and the relevant market surveillance authority, and suspend use. This is a conditional rule tied to the Act’s system classification, the deployer’s role, and the specified risk; it is not a general instruction for every AI incident in every country.
The Act’s serious-incident reporting time limits vary by incident type. Do not apply one deadline to all incidents or infer a deadline from a summary. Before acting on a real incident, check the live consolidated text, the organization’s role, applicable provisions and effective dates, and the competent authority. The European Commission’s overview describes high-risk-system obligations covering risk assessment and mitigation, data quality, logging, documentation, human oversight, robustness, cybersecurity, and accuracy. Because implementation dates and transition periods can change, check the Commission’s current overview alongside the applicable legal text.
Build a response that can be repeated
An isolated incident can reveal a weakness in governance as well as in a model. Organizations can make future response faster by documenting intended uses, assigning stop-use authority, maintaining provider contacts, defining secure evidence handling, and establishing routes for human review and appeals before a problem occurs. They can also define how incident trends are reviewed, how affected people are informed, and what evidence is required before a system returns to service.
NIST says its AI RMF is being revised. The guidance discussed here is the AI RMF framework and Core described in the cited NIST materials; check NIST’s current status when applying it. For EU obligations, the cited consolidated legal version is dated 2026-07-27, and the live text and effective dates should be verified for the specific case.
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