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Freedom251 › Blog › AI ethics

What to Do If an AI System Produces Harmful or Discriminatory Results

By Freedom251 Team · · 9 min read

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If an AI system may be causing harm or discrimination, reduce the risk of further impact, preserve a clear record, and get the decision in front of accountable people. If you control the system or workflow, consider pausing or limiting the affected use and routing consequential decisions to qualified human review. If you are affected by a decision, save the notice and use the organization’s appeal or review channel. Legal duties depend on the jurisdiction, the organization’s role, the type of decision, and sometimes the system’s regulatory classification.

First, work out what needs to happen now

Start with the potential harm, not with a debate about whether the AI is technically “biased.” A wrong result can have very different consequences depending on whether it affected a high-stakes decision, how many people may be affected, and whether the same process is still operating. If the outcome could affect someone’s rights, safety, employment, access to services, or another consequential interest, treat it as an incident requiring prompt attention while the facts are checked.

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The right response depends partly on your role. A person subject to a decision may not control the system, but can preserve the decision and request review. An organization that deploys or operates a system may be able to pause a feature, route cases to staff, notify a provider, and investigate. A provider may have a separate role in determining whether a technical or product change is needed. Do not assume that the person who sees the result has authority to change the system, or that a vendor can decide how an organization should treat people affected by its workflow.

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Situation Immediate priority
You are affected by an AI-supported decision Keep the decision notice and related records; ask who made or reviewed the decision and how to appeal or request human review.
You operate or deploy the system Assess whether continued use could extend harm; use an appropriate pause, restriction, human review, or non-AI alternative if warranted.
You provide or maintain the system Engage with the deployer’s incident report, preserve relevant technical records, and assess the system behavior and any needed correction.

This is a practical way to organize a response, not a universal legal checklist. NIST’s AI Risk Management Framework (AI RMF) is voluntary guidance, and the NIST page says the framework is being revised. Treat its risk-management outcomes as useful guidance, not as a substitute for applicable law or current organizational procedures.

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If you control the workflow, reduce exposure

Decide whether the affected use can safely continue while the incident is assessed. Depending on its severity and your authority, options may include pausing a feature, restricting it to a narrower use, reverting to a known safer version, requiring qualified human review, switching to a viable non-AI process, or disengaging or deactivating the system. NIST’s AI RMF Core describes mechanisms and assigned responsibilities for superseding, disengaging, or deactivating systems whose performance or outcomes conflict with intended use, as well as procedures to respond to and recover from previously unknown risks.

Choose a measure that actually interrupts the route by which harm could recur. For example, a human reviewer who sees the same output without time, information, or authority to challenge it may not meaningfully reduce reliance on that output. Likewise, stopping one interface may not be enough if the affected result is still passed into another decision process. Map where the output goes and confirm that the chosen safeguard operates at those points.

Record who authorized the response, when it took effect, which uses or decisions it covers, and what alternative process is being used. If you cannot stop the system yourself, promptly raise the concern with the accountable operational owner and ask what interim safeguard is in place.

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If you are affected, preserve the decision and request review

Save the decision notice, message, or other communication you received. Note when and how it arrived, the organization involved, and any stated reason, deadline, or appeal route. Ask the organization to identify the person or team responsible for the decision and explain how to request a meaningful review or appeal. If a deadline is given, treat it as time-sensitive while you seek clarification.

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Describe the part of the outcome you believe is wrong and provide relevant supporting information through the organization’s verified channel. Avoid sending sensitive personal information to an unverified contact. If the organization offers a human review or override process, ask whether the reviewer can reconsider the outcome itself and what information they will consider; an explanation alone may not change a decision.

If the organization does not respond, the issue is urgent, or the decision has serious consequences, consider contacting the relevant rights, employment, privacy, consumer, or sector regulator in your jurisdiction, or seeking qualified legal advice. Which route is appropriate depends on the type of decision and applicable local rules.

Preserve a usable incident record

A good record lets investigators reconstruct what happened without collecting more personal information than necessary. Preserve relevant material under the organization’s ordinary privacy, security, and retention controls. Do not casually circulate sensitive records or expose them in an informal discussion channel.

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  • When: date and time of the output, decision, discovery, and any containment action.
  • Which system: system or vendor name and version if known, the feature used, and the task it was performing.
  • What happened: relevant inputs and outputs, the decision or action taken, and any explanation shown to a user.
  • Who may be affected: the individual or groups potentially affected, using only the details needed for investigation.
  • Human involvement: whether someone reviewed, overrode, accepted, or appealed the output, and what they did.
  • Response: notifications, restrictions, fixes, review decisions, and the people responsible for each action.

Keep the record factual. Separate what is known from what is suspected, and do not alter original logs or messages when preserving them. If a record must be redacted or copied for wider access, retain an appropriately protected original according to the organization’s controls. NIST calls for incident and error tracking, documentation, communication, response, and recovery; the fields above are practical recommendations rather than a mandatory NIST template.

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Escalate to people who can act

Notify the operational owner and, where relevant, the system provider. Bring in the people responsible for risk, legal obligations, privacy, safety, security, discrimination or employment compliance, and communications with affected users. The exact group depends on the sector and impact. Assign one person to coordinate the response so that the same incident is not investigated in disconnected channels.

Share enough to make the report actionable: what use was involved, what outcome occurred, when it happened, how it may have caused harm, whether the use is continuing, and what records are available. Avoid making a broad claim about intent or discrimination before the facts are assessed. At the same time, do not dismiss a credible concern simply because a system’s average accuracy appears acceptable.

NIST’s AI RMF Core calls for incidents and errors to be communicated to relevant AI actors, including affected communities, and for responsibilities in response and recovery to be understood. Decide who needs to be told, through which channel, and when; communication should be accurate and should not disclose other people’s sensitive information unnecessarily.

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Investigate the result in its real context

An average performance score can conceal a harmful pattern for a particular group, task, or setting. Investigate both the system and the workflow in which it was used. NIST describes trustworthiness considerations across pre-design, development, deployment, use, testing, and evaluation. Its harmful-bias work identifies methods to identify, understand, measure, manage, and reduce harmful bias as a standards-development priority. Neither source prescribes one fairness metric that works for every system.

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Reconstruct the use

  • What was the system intended to do, and what task did it actually perform?
  • Who relied on the output, and what action or decision followed from it?
  • Could the same outcome affect other people, or recur through another part of the workflow?
  • Was a non-AI alternative available, and could the person affected contest the result?

Check for patterns and contributing causes

Test in representative conditions relevant to the use and the people affected. Where lawful and appropriate, examine outcomes across relevant groups and look for differences in error patterns, access, or treatment. Interpret any comparison in context: data quality, sample size, task definition, and the consequences of errors can all affect what a result means. Do not treat a single metric as proof that a system is fair or unfair.

Examine the complete chain of causes rather than assuming the model alone is responsible. Relevant factors can include input data, design choices, thresholds, how the system was configured, changes made by a provider, how staff were trained, and whether people relied on a recommendation as though it were a final decision. An outcome may arise from the model, from the way it was deployed, or from their interaction.

Keep findings traceable: document what was tested, which conditions were examined, what was observed, and what remains uncertain. NIST’s bias materials support context-sensitive identification and management; they do not establish a single universal test or guarantee that passing a test eliminates harmful bias.

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Correct the cause and verify the response

Choose a treatment that addresses the identified problem and the path through which it affected people. Depending on the findings, the organization might change the workflow, add meaningful human review or an appeal route, modify data or thresholds, switch systems, disable the affected feature, or end the use. A technical change is not automatically an adequate remedy if the affected decision process remains unchanged.

Before returning the affected use to operation, assess whether the response addresses the impact and who is accountable for checking it. Define what will be monitored, how appeals or overrides will be handled, how recurrence will be escalated, and who can suspend the use again. Where people received harmful decisions, consider how they will be informed and whether decisions need reconsideration; the appropriate remedy depends on the case and applicable obligations.

NIST treats post-deployment management as continuing work, including monitoring user input, appeal and override, incident response and recovery, change management, and decommissioning. It also calls for communicating incidents and errors to relevant AI actors and affected communities. A correction therefore needs follow-through in the workflow, not only a change to a model or configuration.

Legal duties depend on the system, role, and jurisdiction

There is no single reporting rule for every harmful or discriminatory AI result worldwide. Duties may depend on country, sector, decision type, the organization’s role, and whether the system is within a regulated category. NIST’s AI RMF is voluntary guidance; it does not itself create a general U.S. or global reporting obligation. For a consequential decision or ongoing harm, consult the relevant regulator or authority and qualified counsel for the jurisdiction and sector.

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EU example: a conditional rule for high-risk AI

The consolidated text of Regulation (EU) 2024/1689 identified as version 02024R1689-20260727 includes a specific rule for deployers of high-risk AI systems. If a deployer has reason to consider that use in accordance with the instructions may present the risk described in Article 79(1), it must, without undue delay, inform the provider or distributor and the relevant market surveillance authority, and suspend use. This is not a general rule for every AI incident or every system.

The Act also has serious-incident reporting periods that vary by incident category, including different outer limits for ordinary serious incidents, widespread infringements or specified serious incidents, and incidents involving death. The applicable timing and steps depend on the incident and the current legal text; do not infer a deadline from the broad description here. An organization responding to a real event should check the live consolidated law, its role, the provisions and dates in force, and the competent authority.

The European Commission’s AI Act overview describes requirements for high-risk systems that include risk assessment and mitigation, dataset quality, activity logging, documentation, human oversight, robustness, cybersecurity, and accuracy. It also presents transition periods and dates. Because regulatory implementation timelines can change, verify the Commission’s current information and the legal text before relying on a date.

Sources and scope

This article is general information, not legal advice. Its operational response guidance draws on NIST’s AI Risk Management Framework FAQs and AI RMF Core, including the framework version and status described on NIST’s pages, and NIST’s materials on identifying and managing harmful bias in AI. The legal example refers to the consolidated EU AI Act text identified above and the European Commission’s AI Act overview. Verify current requirements before acting on a live incident.

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AI ethicsAI risk managementalgorithmic biasArtificial IntelligenceConsumer Rights

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