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An AI-generated performance review is a draft, not evidence. Check every material statement against dated work records and written expectations, test whether comparable employees would be judged by the same standard, remove unsupported or disability-sensitive inferences, and rewrite broad labels as observable behavior with a clear next step. The process below is a practical review aid—not a validated bias test or a substitute for HR or legal advice in a consequential case.
Start with a sentence-level audit
Read the review one sentence at a time, especially sentences that rate, criticize, praise, or recommend action. Do not let fluent wording create a presumption that the underlying claim is true. Put each sentence into a worksheet with six checks:
| Check | Question to ask | What passes |
|---|---|---|
| Evidence | What dated, first-hand example or reliable record supports this? | A project record, approved metric, ticket, deliverable, meeting note, or documented observation that falls within the review period. |
| Standard | Which written expectation or criterion is being applied? | A role-relevant goal, competency, service level, policy, or agreed objective. |
| Observation | Does the sentence describe behavior or results rather than personality or motive? | Something another reviewer could verify, such as a missed handoff or delivered result. |
| Context | Are workload, changing responsibilities, dependencies, and approved accommodations represented? | Relevant constraints and contributions are included without speculation about protected information. |
| Consistency | Would a comparable employee be assessed with the same evidence and wording? | The same standard, level of detail, and treatment of similar conduct. |
| Action | Does the employee know what to continue, change, or ask about? | A specific, attainable next step and a way to measure or review it. |
Mark a sentence for correction when any answer is “no” or “unknown.” A polished sentence with no support is still an unsupported sentence.
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Verify facts before judging quality
HHS performance-management guidance calls for feedback that is accurate, factual, complete, and based on established expectations. Apply that discipline to every factual detail in an AI draft.
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Check identity, scope, and dates
- Confirm the employee’s name, job title, team, manager, and reporting relationship.
- Check that projects, customers, products, systems, and quoted comments actually belong to the employee.
- Confirm the review period. Remove achievements or incidents outside it unless the policy explicitly allows them.
- Check whether a result was individual, shared by a team, or dependent on another group.
Reconcile metrics and outcomes
Trace every number to its original source. Verify the definition, time window, baseline, denominator, and whether the figure was later corrected. “Reduced response time by 30%,” for example, is incomplete without knowing which queue, dates, and measurement method were used. If the record cannot be found, change the statement to a qualified observation or remove it.
Authenticate quotations and incidents
Compare quoted feedback with the original message, survey, ticket, or meeting record. An AI system can merge comments, change tone, or attribute a team statement to one person. Never retain a quotation that cannot be authenticated. For an incident, record what happened, when, who observed it, what expectation applied, and what outcome followed.
Separate evidence from interpretation
Rewrite “She is unreliable” into the underlying facts: “Two of four scheduled status updates arrived after the agreed deadline in May; the launch checklist was consequently updated one day later.” If the record does not establish the claimed impact, do not invent one to make the sentence sound complete.
Check that each rating is relevant to the job
Compare each rating with the employee’s documented role expectations and performance criteria. A general impression such as “not strategic” cannot substitute for a criterion that explains what strategic performance means in this role.
Use the written expectations
Gather the job description, current goals, competency framework, project agreements, and any mid-cycle changes. For each review statement, identify the exact expectation it addresses. If no relevant expectation exists, ask whether the statement belongs in the review at all.
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Account for changed responsibilities
Roles, priorities, staffing, and dependencies can change during a cycle. Note when a goal was added, withdrawn, or made impossible by a decision outside the employee’s control. Do not score an employee against a target they were never given or that was formally replaced.
Standardize the rating process
EEOC guidance on selection procedures says overall job-performance ratings should be carefully developed and their use standardized and controlled. That guidance concerns selection procedures, so it should not be presented as a universal rule for every performance review. Its practical lesson is narrower: define rating criteria in advance, apply them consistently, document who made the judgment, and control discretionary exceptions.
Look for bias signals without declaring a legal finding
Wording review can identify risk signals; it cannot, by itself, prove unlawful discrimination. OECD’s 2024 Using AI in the Workplace discusses performance assessment as a possible site of AI-related bias, particularly when system design or training data are biased or non-representative. NIST Special Publication 1270, published March 15, 2022, likewise explains that bias can cause harmful impacts even when no harm was intended.
Flag stereotypes and personality labels
Highlight terms that may encode stereotypes or cultural preferences, including “aggressive,” “emotional,” “difficult,” “not a culture fit,” “lacks polish,” or “not strategic.” A label is not automatically biased, but it demands an observable definition. Ask what behavior the writer means, whether it affected work, and whether the same behavior receives the same description for others.
Test for unequal standards
Compare the draft with reviews for employees in comparable roles. Examine:
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- Whether assertiveness is praised in one person and called difficult in another.
- Whether mistakes are treated as learning opportunities for one employee but as character flaws for another.
- Whether collaboration, visibility, communication style, or availability is treated as a requirement when it is not in the role criteria.
- Whether credit for team outcomes and blame for team failures are allocated consistently.
Use comparable evidence, not word-count parity, as the test. Equal numbers of paragraphs do not establish fairness.
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Ask whether the employee could reasonably control the outcome. A delayed release may reflect an upstream dependency, changed requirements, or a staffing gap. The review should distinguish the employee’s decisions from conditions they could not control and should credit documented risk escalation or mitigation.
Review disability and accommodation context carefully
Employment uses of AI and software can create accommodation, screening, and disability-inquiry risks. EEOC and Department of Justice guidance issued May 12, 2022 addresses these concerns in the United States; laws and procedures differ elsewhere.
Ask whether the process itself could disadvantage someone
Consider whether the tool relies on speech patterns, facial expression, typing speed, attendance patterns, response time, or other proxies that could disadvantage a person with a disability. A review generated from such signals needs human scrutiny and a check that the signal is actually job-related.
Respect accommodation boundaries
Confirm that approved accommodations and modified processes were considered when interpreting an outcome. Do not include a diagnosis, medical detail, or speculation about a disability in the review. If the reviewer needs information to apply an accommodation, use the organization’s confidential HR process rather than placing sensitive details in performance prose.
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Escalate consequential concerns
If a rating may affect pay, promotion, discipline, termination, or access to work, involve qualified HR, accessibility, or legal support. The checklist can surface questions; it cannot determine whether a particular decision violates law.
Turn vague feedback into observable, actionable feedback
Pennsylvania’s performance-management handbook recommends behavioral feedback, specific examples, and facts. It contrasts verifiable, observable, measurable, actionable material with judgments, assumptions, interpretations, and non-actionable language.
A four-part rewrite pattern
- Behavior or result: State what was seen or delivered, with a date or period.
- Context: Identify the task, expectation, and relevant dependency.
- Work impact: Describe a documented consequence, or say that impact is not established.
- Next step: State what should continue or change and how progress will be checked.
Examples of useful rewrites
| Vague draft | Evidence-based alternative |
|---|---|
| “You lack ownership.” | “The client handoff scheduled for June 12 did not include the final test results, so the receiving team repeated the test on June 13. Before the next handoff, attach the approved test report and confirm receipt in the project channel.” |
| “You are not strategic.” | “In the two roadmap meetings reviewed, proposals addressed immediate fixes but did not include the requested quarterly risks, dependencies, or alternatives. For the next roadmap submission, include those three sections.” |
| “You are difficult to work with.” | “In the May 8 design review, two colleagues reported that decisions were reopened after the agreed deadline. Use the decision log to record a new concern and raise it with the owner before the deadline.” |
| “Communication needs improvement.” | “Three weekly status updates in April omitted the risk owner and expected resolution date. Use the team template for both fields in every update through the next review checkpoint.” |
Use only examples you have verified. If you cannot substantiate the event or impact, do not fill the gap with a plausible-sounding invention.
Compare two AI-generated drafts systematically
When managers receive multiple drafts, score the drafts on the same dimensions rather than choosing the most fluent one.
| Dimension | Questions |
|---|---|
| Evidence quality | Are claims traceable to dated records and authentic observations? |
| Role connection | Does each rating map to a current, relevant expectation? |
| Specificity | Could the employee identify the behavior, result, and time period? |
| Comparable treatment | Would the same evidence produce similar wording for a comparable employee? |
| Context | Are dependencies, changed priorities, workload, and accommodations represented? |
| Actionability | Is there a clear, measurable next step? |
| Transparency | Is AI assistance disclosed internally, with a named human reviewer and a correction route? |
Do not convert these dimensions into an invented pass mark. The available guidance does not establish a universal numeric threshold or a validated prose-only test for AI-written reviews.
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Make the process transparent and challengeable
OECD employment guidance emphasizes transparency, explainability, human determination, traceability, and risk management. OECD principles call for meaningful information that helps affected people understand outcomes and challenge them.
Document AI’s role
Record whether AI generated a first draft, summarized records, suggested ratings, or merely corrected grammar. List the information supplied to it and the human who verified the final text. Do not imply that a manager personally observed an event if the sentence came from an unverified machine summary.
Keep an audit trail
Retain the source records, criteria used, material edits, reviewer names, and date of approval according to your organization’s retention rules. Traceability makes it possible to correct an error without relying on memory.
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Provide a correction route
Tell the employee how to challenge an inaccurate statement, supply contrary evidence, request an accommodation review, or ask which criterion supports a rating. A challenge process should identify who reviews the issue, what records are considered, and when the person will receive a response.
A repeatable review procedure
- Collect the evidence set. Assemble the review-period goals, role criteria, project records, approved metrics, feedback sources, accommodation-related process information handled through the proper confidential channel, and comparable review standards.
- Lock the scope. Confirm the employee, role, review dates, and any formally changed objectives before reading the prose for tone.
- Annotate every evaluative sentence. Mark its evidence, standard, observation, context, consistency, and action status.
- Correct factual errors. Remove unsupported names, dates, quotations, metrics, incidents, and causal claims.
- Run a consistency check. Compare treatment of comparable behavior and outcomes without exposing unnecessary personal information.
- Screen for bias and disability risk. Replace stereotypes and personality inferences with verified behavior; route accommodation or legal questions to qualified specialists.
- Rewrite for action. Add a verified example, documented impact, expectation, and measurable next step where appropriate.
- Record AI and human review. Note what the tool did, who checked it, and how corrections can be requested.
- Obtain the required approval. Follow your organization’s HR and jurisdiction-specific procedures before using the review in a consequential decision.
Common failure modes and fixes
- Fluency bias: The prose sounds authoritative, so the reviewer skips verification. Fix: require a source record for every material evaluative claim.
- Proxy evaluation: The draft rewards responsiveness, facial expression, accent, or visibility rather than job results. Fix: map the criterion to an essential job expectation and test for disability impact.
- Context deletion: The draft records a missed goal but omits changed scope or dependency. Fix: add the documented constraint and distinguish control from circumstance.
- Personality shorthand: “Difficult” or “not strategic” replaces a conversation about behavior. Fix: use the four-part rewrite pattern.
- False precision: A made-up percentage or exact impact makes an anecdote look measured. Fix: verify the metric or state that impact is not established.
- Automation overreach: The tool’s rating is treated as a decision. Fix: require human determination, traceability, and a correction route.
- Confidentiality leakage: Sensitive disability or medical information is pasted into a prompt or review. Fix: follow data-minimization and confidential HR procedures; include only job-relevant information.
What the available guidance can—and cannot—establish
The cited guidance supports factual, expectation-based, behaviorally specific, transparent review practices. It does not provide a universal error rate for AI-written performance-review prose, a prevalence statistic for bias in such text, or a single validated audit instrument. A clean wording review cannot establish that a decision is lawful, nor can a problematic phrase alone prove discriminatory intent. Use qualified HR, accessibility, or legal advice when the review affects employment status or raises a discrimination concern.
Further reading on feedback practice
The American Psychological Association lists Using Feedback in Organizational Consulting by Jane Brodie Gregory, Paul E. Levy, and Brodie Gregory Riordan as a 152-page paperback published in February 2015. Its publisher description presents evidence-based tactics for improving feedback exchanges at work. It is broader feedback reading, not an AI-bias detector or a validation product; check current availability before obtaining it.
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