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Stop relying on the output, verify the claim independently, record what happened, and use your employer’s approved reporting route. Do not forward a questionable answer as fact or use it alone for a consequential decision. Preserve enough context for a reviewer to reproduce the concern, while protecting personal, confidential, and regulated information.
A wrong answer is a factual or technical error. A biased answer may be inaccurate, stereotyped, unfairly framed, or applied unevenly to people or groups. The same response process handles both, but the evidence you collect and the urgency of escalation depend on who could be affected and how.
What to do in the first few minutes
- Pause use of the output. Treat it as unverified. Do not paste it into a report, send it to a customer, approve a transaction, rank a candidate, deny a request, or make another high-impact decision on its authority.
- Preserve the original context safely. Note the tool name, model or version if shown, date and time, work purpose, prompt, output, and any files or settings that materially affected the result. Follow your organization’s rules before copying or exporting content.
- Tell the owner of the decision if action is imminent. A short warning such as “The automated suggestion is unverified and may contain an error; please hold the decision while I check it” prevents an unsupported answer from becoming an official record.
Do not attempt to “test” the tool by entering real customer, patient, employee, financial, legal, security, or other regulated information into an unapproved service. Use a sanitized example or the organization’s approved testing environment instead.
Wrong, biased, or both?
Use precise language. “It looks biased” is a useful alarm, but a reviewer needs to know what is wrong and who may be affected.
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| Issue | What it can look like | Evidence to collect |
|---|---|---|
| Factual or technical error | A false date, invented policy, incorrect calculation, unsupported citation, or instruction that would not work in the stated system. | The authoritative document, current data, calculation, test result, or qualified reviewer that contradicts the output. |
| Unfair or stereotyped framing | An assumption about a person or group, a degrading description, or a recommendation based on a protected or irrelevant characteristic. | The exact wording, the relevant workplace rule or standard, and an explanation of why the framing is inaccurate, harmful, or irrelevant. |
| Uneven treatment | Different quality, thresholds, opportunities, or explanations for comparable people, groups, languages, locations, or accessibility needs. | Comparable examples, the criteria used, and the impact on each group. A single example warrants investigation but does not by itself prove a systematic pattern. |
| Automation bias | People accept a confident-looking answer without an effective human review. | Where the output entered the workflow, who relied on it, what review was skipped, and any resulting decision or harm. |
The Information Commissioner’s Office (ICO) describes automation bias as a risk in which “humans involved assume AI is right by default and do not effectively review decisions or outputs.” Confidence, fluent prose, or a citation-like format is not evidence of correctness.
A complete response sequence
1. Pause reliance without destroying evidence
Stop the immediate use of the answer, but do not casually delete the conversation or overwrite the source file. If the tool is embedded in a ticketing, hiring, analytics, or content system, mark the item as pending human review according to local procedure. Preserve only what policy permits; screenshots or exports can themselves contain sensitive data.
If the output has already been sent externally, tell the responsible colleague or manager promptly. Ask them to hold publication, decision, or enforcement until the claim is checked. If a safety, security, financial, employment, health, or legal consequence may be imminent, use the organization’s urgent incident channel rather than waiting for a routine meeting.
Do these 3 things before closing this tab:
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Break the answer into individual claims. Verify each one against the primary policy, contract, product documentation, official record, current dataset, or qualified subject-matter reviewer. Check dates, jurisdiction, version, units, assumptions, and whether the source actually supports the conclusion.
- For a policy question, use the current approved policy and ask the policy owner to interpret ambiguous language.
- For a calculation, recreate it with an approved spreadsheet or script and show the inputs and units.
- For a technical instruction, test it in a safe environment and record the software version and error message.
- For a people-related recommendation, ask an appropriately qualified reviewer to assess the criteria and whether they are job-relevant and consistently applied.
Separate an incorrect statement from an answer that is merely incomplete or outside the tool’s stated scope. Write down the smallest verifiable proposition that fails; this makes triage faster than forwarding a long conversation with no explanation.
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3. Assess impact, affected people, and scope
Ask four questions:
- Could this influence someone’s access to employment, pay, promotion, services, credit, safety, or another important opportunity?
- Does it contain personal, confidential, privileged, export-controlled, or regulated information?
- Does it affect one person, a defined group, or a process used at scale?
- Has anyone already acted on it, and can the action be reversed?
Use the answers to set urgency. A harmless draft typo can wait for normal review; an incorrect eligibility decision or exposed personal data needs immediate containment. A single problematic output is a reason to investigate, not proof of the model’s overall error rate or the cause of the problem. Look for repeatable patterns only through an approved evaluation process.
4. Create a reviewable incident record
Record enough information for another person to reproduce the concern without collecting more sensitive data than necessary. Include:
- Tool, model or version, account or workspace, and relevant configuration, if known.
- Date and time, business purpose, and the workflow stage where the output was used.
- The prompt and output, or a redacted excerpt, where workplace policy allows retention.
- The exact claim, recommendation, or wording you believe is wrong or unfair.
- The source, calculation, test, or reviewer that established the problem.
- People, groups, systems, or decisions that may be affected.
- Actions already taken, such as withholding a message, correcting a record, or notifying a decision-maker.
- Any uncertainty, missing context, or limitation that could change the assessment.
Store the record in the approved case-management, security, privacy, compliance, or quality system. Do not email a broad distribution list or upload evidence to another AI service merely to obtain a second opinion. Redact identifiers when a reviewer can do the job without them, and keep the unredacted version only where authorized.
5. Escalate through the appropriate internal route
There is no universal reporting chain. Start with the organization’s AI-use, information-security, privacy, quality, or incident policy. The right contact may be a manager, tool owner, AI governance lead, privacy officer, compliance team, human-resources team, legal counsel, security operations, or another designated function. Not every employer has all of these roles.
| Decision factor | Likely priority | What to do |
|---|---|---|
| Immediate or irreversible harm | Urgent | Use the emergency or incident channel and notify the person who can stop the decision. State the concrete risk and deadline. |
| Personal, confidential, or regulated data | High | Contact the privacy or security route named by policy. Share the minimum necessary evidence and do not create additional copies. |
| Individual employment or service outcome | High | Ask the decision owner and the relevant people, compliance, or legal function for a human review and a documented correction. |
| Repeated pattern across groups or cases | High to normal, depending on impact | Provide comparable examples and request a formal fairness or quality assessment rather than relying on an anecdotal conclusion. |
| Low-impact draft or isolated error | Normal | Report to the tool owner or manager, correct the work, and record the issue if policy requires trend monitoring. |
When you report, lead with the requested action: “Please pause this workflow,” “Please review the affected records,” or “Please advise whether this data may be retained.” Attach the concise incident record, not an unfiltered dump of unrelated conversations.
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6. Correct downstream work
If the answer was shared or used, identify every place it may have traveled: documents, tickets, dashboards, customer messages, decisions, or automated jobs. Notify the responsible owner and affected decision-makers. Ask for a human re-review, correction of the source record, and a clear notice to anyone who received materially wrong information.
Do not silently edit a consequential record without preserving the correction history. If a person was disadvantaged, ask what remedy the organization’s process provides, such as reconsideration, corrected payment, restored access, or a written explanation. Keep the focus on fixing the outcome, not assigning blame before the facts are established.
How to write a useful report
A good report is specific, proportionate, and easy to investigate. Use a short subject line that identifies the workflow and risk, for example “Unverified automated eligibility recommendation—review requested.” In the opening paragraph state what happened, what is at stake, and the deadline.
Then answer these questions in order:
- What did the tool produce, and for what approved business purpose?
- Which statement, criterion, or recommendation is incorrect, stereotyped, or unevenly applied?
- What source or review demonstrates the issue?
- Who or what may be affected, and has any action already occurred?
- What containment or correction have you completed?
- What decision or support do you need from the recipient?
Illustrative wording: “On 16 October 2026, the approved drafting tool suggested that a candidate lacked a required certification. The current certification register shows the certification is valid through June 2027. No hiring decision has been finalized. I have paused the recommendation and request a human review of this candidate and any comparable records processed with the same rule.” Replace the dates, systems, and facts with your own verified information; do not copy the example into a real case.
What official guidance does—and does not—tell an employee
NIST guidance
The National Institute of Standards and Technology’s Generative AI Profile, NIST AI 600-1, published July 26, 2024, is an organizational risk-management resource. It recommends evaluating fairness and harmful bias with methods appropriate to the use case, examining demographic groups and subgroups, considering affected communities, and documenting assumptions and limitations.
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The NIST AI Risk Management Framework is voluntary, not a universal workplace incident procedure. NIST says AI RMF 1.0 is being revised. Use these materials to explain why verification, impact assessment, and documentation are sensible controls; follow your employer’s actual policy for reporting and retention.
ICO guidance
The ICO’s discussion of discrimination and bias is focused on the UK data-protection context and is primarily written for AI engineers and key decision-makers. It identifies insufficiently diverse or inappropriate training data, historical discrimination in data, and design choices as possible sources of unfair outcomes. The page is under review following the Data (Use and Access) Act, so its legal discussion should not be treated as settled law or as a rule for every country.
Neither source supplies one legal answer for every workplace. Whether a particular output violates employment, equality, privacy, consumer-protection, or sector rules depends on the jurisdiction, facts, organization, and decision involved. Ask the designated legal, privacy, compliance, or people team when the stakes are material.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Examples of applying the process
An incorrect policy summary
An AI assistant summarizes a travel policy and states that an expense is reimbursable. You compare the sentence with the current policy and find that the allowance changed last month. You stop the draft from being sent, cite the current section, ask the policy owner to confirm the interpretation, and correct any expense guidance already distributed. Because no personal data or decision about a person is involved, the normal tool-owner reporting route may be sufficient unless the error caused payment or compliance consequences.
A potentially biased hiring recommendation
A screening tool describes one applicant as “not a culture fit” while giving similarly qualified applicants specific evidence. You preserve the output under the approved hiring record, identify the inconsistent criterion, and ask for a human review using job-relevant requirements. You do not infer a system-wide bias from one case, but you provide comparable examples if the owner requests a fairness assessment. The hiring decision remains on hold until the authorized reviewer completes the process.
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An answer containing confidential customer data
A support assistant reproduces details from another customer’s account. Stop copying or sending the response, restrict access to the conversation, and use the organization’s privacy or security incident route immediately. Do not paste the exposed details into a public chatbot or forward them to colleagues who do not need them. Record what was exposed, to whom, and when, using the minimum necessary detail.
Preventing repeat problems in your team
After the immediate case is contained, ask the tool owner what control will reduce recurrence. Practical controls include:
- A documented list of approved tools, data types, and prohibited uses.
- Human review requirements for decisions affecting people, money, safety, rights, or access.
- Clear ownership for model configuration, source data, prompts, and final decisions.
- Versioned prompts and evaluation sets that include relevant demographic groups, languages, accessibility needs, and edge cases where appropriate.
- A way to record errors, near misses, corrections, and unresolved limitations.
- Training that explains automation bias and teaches staff to challenge confident but unsupported output.
- Access controls, retention limits, redaction, and deletion procedures for conversations and attachments.
These controls support review; they do not turn an AI answer into an authoritative source. A reviewer should be able to explain the final decision using approved evidence without relying on the model’s confidence or fluency.
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Use an external professional or regulator only through the channels appropriate to your situation and jurisdiction. Consider independent legal advice when the issue involves discrimination claims, employment action, contractual duties, regulated decisions, or a potential data breach. Consider a qualified technical or accessibility reviewer when the concern depends on a specialized test. Do not disclose confidential workplace material externally unless authorized or legally required.
If an internal route is unclear, ask your manager or information-governance contact which policy applies and where to submit the record. Keep a dated copy of your report and responses in the approved system so the organization can show what was reviewed and corrected.
Quick Recap
A compact checklist
- Pause reliance and prevent the output from driving a consequential action.
- Verify each material claim against authoritative evidence.
- Describe the specific error, stereotype, or uneven treatment.
- Assess affected people, groups, data sensitivity, urgency, and reversibility.
- Record tool, version, date, prompt, output, evidence, impact, and actions, subject to policy.
- Escalate to the route that owns the risk, decision, data, or tool.
- Notify decision-makers and correct downstream records or communications.
- Request a documented human review and a control to reduce recurrence.
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