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If an AI tool produces an answer that appears wrong or unfair, pause before using it. Save the original prompt and output where your workplace rules allow, verify each important claim against authoritative evidence, assess who could be harmed, record what you found, and report it through the right internal channel. A fluent answer is not proof of accuracy, and one troubling result is not, by itself, proof of unlawful discrimination.
1. Stop the output from becoming an unchecked decision
Do not forward a suspect answer as fact or let it silently determine a customer response, payment, safety action, compliance position, public statement, or employment decision. Put the task on hold long enough for a person with appropriate knowledge and authority to review it.
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- Preserve the evidence. If permitted by company policy, retain the exact prompt, output, date, time, tool name, and version or model identifier. A screenshot alone may omit context, so save the surrounding conversation or export it using an approved method.
- Protect confidential information. Do not paste employee records, health information, customer data, trade secrets, or investigation material into another unapproved AI service merely to check the first result. Use your organization’s approved systems and privacy procedures.
- Mark the status clearly. Label the material as unverified, draft, or under review so another person does not mistake it for an approved conclusion.
- Identify the decision owner. The person accountable for the outcome—not the software—must decide whether work can continue, what evidence is required, and whether the result should be discarded.
NIST’s guidance on generative AI describes confabulation (a plausible but false output) and automation bias, in which people over-rely on a system. Human intervention is especially important when the system cannot detect or correct its own error.
2. Verify every material factual claim
Verification works best when you stop treating the answer as one block of text. Split it into individual claims and test each claim separately. Start with primary material: the current company policy, signed contract, regulation, court or agency record, official dataset, product specification, or original research paper.
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- List the claims. Underline statements about dates, names, quantities, eligibility, deadlines, quotations, calculations, or causes. Separate a factual assertion from the tool’s interpretation or recommendation.
- Find independent evidence. Prefer the document or record that created the rule. For a legal requirement, check the current government or court source; for a company rule, check the controlled policy repository rather than an old slide deck.
- Check scope and currency. Confirm the jurisdiction, effective date, edition, population, and assumptions. An answer can be accurate in one country, business unit, or policy version and wrong in another.
- Test calculations and quotations. Recalculate totals, percentages, dates, and conversions yourself. Search the original source for quoted language and read enough surrounding text to see whether the quote changes meaning.
- Check links and citations. Open each cited source and confirm that it actually supports the sentence. A real-looking citation can point to a different topic or an outdated page.
- Separate fact, inference, and uncertainty. Write down what is confirmed, what is a reasonable interpretation, and what remains unknown. Do not turn a probability or suggestion into a definite statement.
| Claim type | Useful check | If it cannot be confirmed |
|---|---|---|
| Policy, contract, or legal requirement | Read the current controlled text and confirm jurisdiction and effective date. | Pause the decision and ask the policy owner or qualified legal reviewer. |
| Number, forecast, or calculation | Recompute from the underlying figures and record assumptions. | Show the calculation as unverified; do not publish or approve it. |
| Quote, name, or event | Locate the original record and compare the exact wording and date. | Remove the quote or attribute it only after confirmation. |
| Recommendation or classification | Check the criteria, inputs, exclusions, and comparable cases. | Have a subject-matter expert make the judgment without relying on the output. |
If a material point remains unresolved, say so plainly, remove it, or obtain qualified review. NIST’s Generative AI Profile treats information integrity as distinguishing fact from inference, acknowledging uncertainty, and connecting claims to evidence. It does not say that every generated answer is wrong; it says accuracy and reliability must be evaluated for the intended context.
3. Examine whether the result may be biased
Bias is not limited to insulting language. An output can appear neutral while using a proxy for a protected characteristic, omitting relevant evidence, applying a stricter standard to one group, or producing a disadvantage that is visible only when results are compared across people and contexts.
Ask what the system was asked to do
Review the prompt, instructions, reference documents, and data fields. Was the task framed around a vague idea such as “culture fit” or “professionalism”? Did the system receive complete information, or were language, disability, schedule, location, or career-break details missing? A narrow prompt can encode an unfair assumption before the model generates anything.
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Check whether apparently neutral inputs stand in for characteristics the organization should not be using. Examples include a school or postcode used as a shortcut for socioeconomic background, writing style used as a proxy for nationality or disability, or uninterrupted work history treated as a requirement when it is not necessary for the job. Compare the same type of case under the same evidence standard. Ask whether a comparable person would have received the same recommendation.
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Consider effects, not just wording
Review outcomes for relevant groups and accessibility needs where the organization is allowed to do so. A single troubling result warrants investigation, but it does not establish the cause or prove unlawful discrimination. NIST describes three interacting sources of bias:
- Systemic bias: rules, institutions, or historical practices that produce unequal outcomes.
- Computational and statistical bias: limitations in data, labels, sampling, objectives, or model behavior.
- Human-cognitive bias: assumptions made by designers, managers, reviewers, or users, including over-trusting a confident answer.
Fairness is context-dependent and cannot be reduced to one score. Record the concern as a testable question—such as whether similarly qualified applicants are screened out at different rates—rather than declaring a legal conclusion from one output.
4. Create a concise, factual record
Documentation lets a reviewer reproduce the problem and prevents a vague complaint from becoming a rumor. Keep the record in the approved system, restrict access to people who need it, and avoid copying sensitive personal information into an incident ticket unless policy requires it.
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- Tool details: product and version or model identifier if available, account or configuration, and any retrieval sources or plug-ins used.
- Exact interaction: the prompt, relevant files or fields, output, and any follow-up prompts that changed it.
- Observed problem: the specific false claim, missing context, inconsistent standard, proxy, or concerning pattern.
- Independent check: documents, calculations, comparison cases, or expert comments used to test the output.
- Potential impact: who could be affected, how serious the consequence could be, and whether a decision has already been made.
- Containment and notification: what you stopped, corrected, or disclosed, and which people or teams were informed.
There is no universal employee incident form. Follow your employer’s reporting policy and use the route that matches the risk: a manager or tool owner for a routine correction; an AI governance, compliance, privacy, or security team for system issues; and HR or legal for employment matters.
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5. Escalate decisions that affect people’s rights or livelihood
Require human review before an AI output is used as determinative evidence for hiring, screening, performance evaluation, pay, promotion, discipline, termination, scheduling, access to benefits, or a disability accommodation. Raise an unresolved concern before the output is entered into a personnel record or communicated as the reason for an action.
In the United States, the Equal Employment Opportunity Commission (EEOC) and the Department of Justice have warned that employer use of software and AI can create disability-discrimination risks. A tool may screen out a qualified person, fail to account for a reasonable accommodation, or measure a disability-related trait that is not necessary for the job. Existing employment protections can apply when AI is involved. EEOC Chair Charlotte A. Burrows put the principle plainly in the agency’s May 12, 2022 technical-assistance announcement: “New technologies should not become new ways to discriminate.”
That guidance is U.S.-specific general information, not a determination of any individual case. Laws, procedures, and reporting options differ by jurisdiction. If you believe an employment decision may have discriminated against you, use your organization’s complaint process and consider obtaining advice from a qualified employment or disability-rights professional. Do not assume that an AI error automatically proves a legal violation, and do not let an employer treat the tool’s confidence as a defense.
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6. Give the organization a correction path
For a one-off drafting mistake, correction and coaching may be enough. Repeated or high-impact failures require a system response rather than a series of private prompt edits. NIST’s AI Risk Management Framework (AI RMF) 1.0, released January 26, 2023, is voluntary and is being revised; its functions provide a useful structure, not a mandatory employee incident procedure.
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Govern
Define permitted and prohibited uses, name the accountable decision owner, set retention and privacy rules, and specify who can stop a deployment. Make clear that a human reviewer cannot simply rubber-stamp an automated recommendation.
Map
Describe the task, users, affected people, data sources, failure modes, accessibility needs, and legal or contractual constraints. Decide what “good enough” means for this particular use before selecting a tool.
Measure
Test representative cases and relevant groups under expected conditions. Measure validity and reliability, error patterns, fairness-related impacts, explainability, privacy, security, and resilience. Keep a record of test data, assumptions, thresholds, and limitations.
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Prioritize risks, add controls, monitor after updates, investigate complaints, and decide whether to continue, restrict, replace, or retire the use. Provide a route for users and affected people to request correction or human reconsideration.
Changing a prompt may reduce a visible symptom, but it cannot guarantee that a data, design, or governance problem is cured. Treat prompt changes as a controlled change that must be retested, not as proof that the system is now fair.
7. Evaluate a tool before continuing workplace use
Ask for evidence tied to the actual task instead of relying on a broad vendor performance claim. NIST treats trustworthiness characteristics as interrelated: improving one does not guarantee a safe overall system, and trade-offs may be necessary.
| Evaluation area | Questions to answer |
|---|---|
| Validity and reliability | How accurate is it for this task, under expected conditions, and with what test population and error tolerance? |
| Group and context effects | Were relevant groups, languages, disabilities, edge cases, and work conditions examined? What unequal impacts were found? |
| Transparency and explainability | Can users see limitations, source evidence, input dependencies, and reasons to accept or reject an output? |
| Human control | Is a named reviewer able to stop, correct, override, and document a result before it affects someone? |
| Privacy and security | Does the data flow comply with organizational requirements for personal, confidential, and sensitive information? |
| Accountability and monitoring | Who investigates failures, handles complaints, approves updates, and decides whether the use continues? |
8. Apply the workflow to common situations
An AI summary misstates a company policy
Stop distribution, compare each statement with the current policy repository, correct the draft, and tell recipients which version controls. Record the prompt and policy version if the error recurs, then ask the tool owner whether retrieval settings or source-document controls need attention.
A hiring tool rejects a qualified applicant
Do not treat the rejection as final. Preserve the inputs and output, arrange an independent human review, check whether the criteria were job-related and consistently applied, and involve recruiting leadership, HR, compliance, or legal. Consider accommodation and accessibility issues before any adverse action.
An AI assistant recommends different treatment for similar employees
Pause the action, compare the cases using the same evidence, document the difference, and limit circulation of personal data. Escalate to HR or the designated governance owner; a single result calls for investigation, while a repeated pattern calls for structured testing and remediation.
9. A short decision checklist
- Could this output affect a person, money, safety, compliance, privacy, or public trust?
- Have I preserved the original interaction without violating data-handling rules?
- Which claims are verified, which are inference, and which remain uncertain?
- Could missing context, a proxy, unequal standard, or accessibility barrier create a disparate impact?
- Who is the accountable human decision owner?
- Have I documented the issue and used the organization’s designated reporting route?
- Does the risk require HR, compliance, privacy, security, legal, or an AI governance review before work continues?
The safest default is simple: use AI to assist analysis, not to replace accountability. A careful pause, evidence-based check, proportionate record, and timely human escalation protect both the quality of the work and the people affected by it.
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