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What a sound review check must establish
A reliable review should let an employee answer six questions: What happened? When did it happen? What expectation or goal applied? What was the work impact? Was the same standard used for comparable employees? What should continue or change?
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AI can make unsupported conclusions sound authoritative. It may also compress mixed evidence into a confident paragraph, infer motives from outcomes, or repeat patterns in the information used to generate the draft. OECD’s 2024 Using AI in the Workplace identifies performance assessment as a possible site of AI-related bias, especially when system design or training data are biased or not representative. NIST Special Publication 1270 similarly warns that bias can produce harmful impacts even when nobody intended to discriminate.
No source establishes a universal error rate, bias percentage, word-count rule, or prose-only test for AI-generated reviews. The checks here identify evidence and process problems for human correction; they cannot, by themselves, determine whether unlawful discrimination occurred.
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Prepare the evidence before reading the prose
Freeze the review period and role expectations
Write down the exact start and end dates covered. Keep the job description, current goals, team commitments, competency definitions, and any changes in responsibility that took effect during that period. A sentence can be factually true and still be irrelevant if it describes work outside the employee’s role or a goal that was added after the period ended.
Assemble first-hand records
Use source material such as project plans, dated deliverables, quality or service records, customer or peer feedback, meeting decisions, approved metrics, and documented coaching. Record who created each item and when. A manager’s contemporaneous note and a later recollection do not carry the same context, so preserve that distinction.
Capture context that can change the interpretation
Note staffing changes, dependency failures, shifting priorities, workload, leave, new assignments, and approved accommodations. Do not put confidential medical details into the review merely to explain a result. Instead, record the work-related adjustment or expectation that a reviewer is authorized to consider.
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Keep the prompt or workflow, the information supplied to the system, the generated draft, later edits, and the names of human reviewers. This traceability makes it possible to find an incorrect input, explain a disputed sentence, and correct future versions.
Run a sentence-level evidence audit
Mark every sentence that rates, criticizes, praises, or predicts the employee. Descriptions of routine duties may need less scrutiny, but any sentence that could affect pay, promotion, retention, discipline, or development deserves the full check.
| Check | Questions to ask | Red flags | Required action |
|---|---|---|---|
| Evidence | Is there a specific, dated example or reliable record? | “Always,” “never,” uncited numbers, invented quotations, or claims no one can locate | Attach the record, correct the detail, or remove the claim |
| Standard | What written expectation or goal does the sentence apply? | Unwritten preferences presented as requirements | Identify the applicable criterion or rewrite as a suggestion |
| Observation | Does it describe behavior or a result rather than personality or motive? | “Difficult,” “lazy,” “not strategic,” “doesn’t care” | Describe what was seen, heard, delivered, or missed |
| Context | Are dependencies, resources, changing priorities, and approved work adjustments represented? | Credit or blame for events outside the employee’s control | Add material context and separate controllable from uncontrollable factors |
| Consistency | Would a comparable employee be assessed with the same evidence and wording? | Different thresholds, detail levels, or standards for similar work | Calibrate against comparable cases and document the reason for any difference |
| Action | Does the employee know what to continue, change, measure, or discuss? | Broad criticism with no observable next step | Add a feasible action, owner, timeframe, and success measure where appropriate |
1. Verify names, dates, roles, projects, and results
Check spelling, job title, reporting line, project membership, review dates, numerical results, and quotations against the original record. Confirm that a result belongs to this employee and this period, rather than to a team, predecessor, or later quarter. If a metric changed definition, state which definition was used. Remove a number that cannot be reproduced.
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HHS performance-management guidance calls for feedback that is accurate, factual, complete, and based on established expectations. Treat that as a practical quality standard: a polished sentence without a traceable basis is incomplete.
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For each rating, write the matching responsibility or criterion in a separate column. If you cannot make the connection, ask whether the draft is judging style, visibility, or personal preference instead of job performance. EEOC guidance on selection procedures says overall performance measures should be carefully developed, standardized, and controlled. That guidance should be applied narrowly here; it does not create one universal rule for every performance review.
3. Separate observation from interpretation
“The handoff document was delivered two days after the agreed date” is observable. “You lack ownership” is an interpretation that may or may not be supported. An interpretation can remain only when the underlying behavior, standard, and impact are stated and the employee has a fair opportunity to respond.
4. Check the time and causal logic
Do not use a recent incident to characterize an entire year without showing a pattern. Do not treat correlation as cause: a delayed launch may reflect a vendor dependency, an approval bottleneck, or an employee decision. Identify what the employee controlled, what the team controlled, and what was external.
5. Check comparable treatment
Choose genuinely comparable examples: similar responsibility, evidence quality, and circumstances. Compare the threshold for “meets expectations,” the amount of detail, the treatment of mistakes, and the credit assigned for shared results. Word-count parity is not proof of fairness; a shorter review can be fair if its evidence is equivalent, while equal-length reviews can apply different standards.
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6. Check whether the sentence leads to action
A useful criticism identifies the behavior, its work impact, the expected practice, and a next step. The action should be within the employee’s authority and measurable without creating a new, undisclosed standard.
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Look for bias signals without declaring bias from wording alone
Stereotypes and personality labels
Flag words that invoke warmth, aggression, loyalty, cultural style, age, accent, gendered expectations, or presumed attitude rather than work behavior. “Too emotional,” “not polished,” and “not a culture fit” require especially careful unpacking because they can encode a preference unrelated to the job. Replace them with a documented interaction, decision, or result and the relevant expectation.
Unequal standards and shifting thresholds
Check whether one employee is praised for assertiveness while another is criticized for the same behavior, or whether one person’s missed target is explained by context while another’s is treated as a character flaw. Compare the evidence, not just the adjectives. A difference may be justified by role or circumstances, but the reason should be explicit and job-related.
Credit and blame for shared work
AI-generated prose may assign a team outcome to the most visible person or blame an individual for a dependency failure. Map each result to the decisions, deliverables, and authority actually held by the employee. Give team credit where the evidence is collective, and describe an individual contribution only when records support it.
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Biased or incomplete input data
A system can reproduce bias already present in past reviews, complaint records, survey participation, or manager notes. Ask whose work is visible in the input and whose work is missing. High-visibility assignments, customer-facing roles, remote work, shift work, and undocumented support work can be systematically over- or under-represented.
NIST’s report abstract states: “While many organizations seek to utilize this information in a responsible manner, biases remain endemic across technology processes and can lead to harmful impacts regardless of intent.” That is a reason to inspect the data and process, not a finding that any particular sentence is discriminatory.
Check disability and accommodation context
In the United States, EEOC and Department of Justice guidance identifies risks when AI or other software is used for screening, disability-related inquiries, or accommodation decisions. A review process can disadvantage a person if it treats an approved adjustment as poor performance, penalizes communication or scheduling differences unrelated to essential duties, or relies on disability-related information that the reviewer should not use.
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Ask these questions:
- Was an accommodation requested, approved, changed, or delayed during the period?
- Did the system or reviewer evaluate an adjusted process against an unadjusted expectation?
- Does the draft disclose medical information that is unnecessary for performance evaluation?
- Were comparable employees given equivalent flexibility for non-medical reasons?
- Does the employee have a way to correct an inaccurate disability-related assumption?
Keep the analysis tied to work requirements and authorized information. Laws and procedures vary outside the United States, so obtain qualified local HR or legal advice when a review could affect employment status or raises a discrimination concern.
Turn vague feedback into specific, fair feedback
Pennsylvania’s performance-management handbook distinguishes behavioral, specific, factual feedback from judgments, assumptions, interpretations, and non-actionable language. Use its practical logic: name the observable event, give the context and work impact, state the expectation, and define the next step.
| Vague draft | What is missing | Rewrite pattern |
|---|---|---|
| “You lack ownership.” | The decision, handoff, date, impact, and expected practice | On [date], [observable handoff or decision] occurred. The agreed expectation was [standard], and the work impact was [verified impact]. Next time, [specific action] by [time or checkpoint]. |
| “You are not strategic.” | The strategic behavior relevant to the role | In [project or decision], the review did not include [required analysis or stakeholder input]. For the next cycle, include [defined input] before [decision point]. |
| “Communication needs improvement.” | Which audience, channel, timing, and consequence | For [recurring situation], updates arrived [verified timing] rather than [agreed timing], which delayed [documented outcome]. Use [channel] at [cadence] and flag blockers within [time]. |
| “Great collaborator.” | The behavior that should be continued | During [specific work], you [observable contribution], enabling [verified result]. Continue this by [repeatable practice]. |
Do not fill the brackets with plausible details just to make the sentence sound complete. Insert only facts that can be verified, and omit the impact when the record does not establish one. A measurable next step can be a date, deliverable, response time, quality criterion, or agreed checkpoint; it need not be a numerical target if a number would be artificial.
Compare two AI-generated drafts systematically
When managers receive multiple drafts, score the drafts against the same questions rather than choosing the one that sounds warmer or more confident.
| Comparison axis | Strong draft | Weak draft |
|---|---|---|
| Evidence quality | Traceable examples, dates, records, and clearly attributed results | General praise or criticism, unsupported numbers, or unverifiable quotations |
| Connection to role | Each rating maps to a current responsibility or agreed goal | Judges style, visibility, or an undisclosed preference |
| Specificity | Describes behavior, result, context, and impact | Uses labels such as “difficult” or “not strategic” |
| Comparable treatment | Uses the same threshold and evidence standard for similar work | Explains one person’s circumstances but ignores another’s |
| Context | Separates controllable actions from dependencies and changing conditions | Assigns team or system outcomes to one employee without proof |
| Actionability | States what to continue or change and how progress will be checked | Leaves the employee guessing what “improve” means |
| Transparency | Records AI assistance, human review, and a correction route | Presents generated text as unreviewed fact |
Make the review transparent and challengeable
OECD employment guidance emphasizes meaningful information that helps affected people understand outcomes and challenge them, along with traceability. In practice, tell the employee whether AI helped draft the review, what categories of information were used, who verified the text, and where to report an error. The exact disclosure and appeal duties depend on jurisdiction, sector, contract, and the decision being made.
Give the employee a usable correction path: identify the disputed sentence, submit supporting records, receive a human response, and preserve the corrected version. A disagreement is not automatically proof that the employee is right, but a process that cannot accept correction cannot reliably distinguish an AI error from a genuine performance issue.
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Use a human sign-off workflow
- Define the scope. Confirm the employee, role, review dates, decision stakes, and applicable criteria.
- Collect and label evidence. Separate first-hand records, team results, feedback, assumptions, and information that should not be used.
- Run the sentence audit. Check facts, standards, observations, context, consistency, and actionability.
- Run the bias and accommodation checks. Examine stereotypes, unequal thresholds, shared credit, dependencies, and authorized accommodation context.
- Rewrite and calibrate. Replace unsupported labels with verified behavior and compare the result with genuinely comparable reviews.
- Obtain accountable approval. A manager with knowledge of the work and an appropriate HR or review process should approve consequential ratings; AI should not be the final decision-maker.
- Disclose and retain the trail. Keep the inputs, draft, edits, evidence, decision, and correction route according to applicable retention rules.
Common failure modes and fixes
“The manager recognizes the wording, so it must be accurate.”
Familiar prose can still contain a wrong date, merged projects, or a conclusion copied from an old review. Require a record for every material evaluative claim.
“The review is balanced because it has praise and criticism.”
Positive and negative paragraphs do not prove fairness. Check whether both sides use comparable evidence and standards, and whether criticism is actionable.
“A neutral tone removes bias.”
Polite language can still encode a stereotype or unequal threshold. Inspect the underlying expectation, examples, and source data.
“More detail is automatically better.”
Length can amplify an unsupported story. Prefer fewer, traceable examples over a comprehensive-sounding narrative built from inference.
“A disclaimer solves the legal risk.”
Calling text “AI-assisted” does not cure an inaccurate rating, an improper disability inquiry, or a discriminatory standard. Correct the process and the decision, not just the label.
Know what this audit cannot decide
This checklist is not a validated instrument, legal threshold, statistical fairness test, or substitute for qualified HR, accessibility, or legal advice. It cannot establish intent, prove disparate treatment from wording alone, or determine whether a particular employment action is lawful. If the review affects termination, compensation, promotion, or a protected-rights complaint, preserve the records and obtain advice appropriate to the governing jurisdiction.
For broader feedback practice, the American Psychological Association lists Using Feedback in Organizational Consulting by Jane Brodie Gregory, Paul E. Levy, and Brodie Gregory Riordan, a 152-page paperback published in February 2015. It addresses evidence-based feedback exchanges generally; it is not an AI-bias detector.
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