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There is no single worldwide disclosure rule. The U.S. Equal Employment Opportunity Commission (EEOC) and Department of Labor (DOL) materials are federal guidance and recommendations, while the UK Information Commissioner’s Office (ICO) guidance addresses data protection and workplace monitoring. The right notice depends on the employer’s location, the system’s function, the data involved, and the effect on the employee.
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What an AI-performance-review disclosure is supposed to do
A useful notice lets an employee understand a specific decision that concerns them, not merely that the employer has purchased an AI product. It should answer four practical questions:
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- What review task does the system perform?
- What categories of information can it use?
- How can its output affect the review, rating, pay, promotion, discipline, or development plan?
- Who checks the information and handles a challenge?
The ICO says organizations should be open about when and why they use AI-enabled decisions and make people aware of a specific AI-enabled decision concerning them in advance. Its explanation should be meaningful and given at a time when the person can understand and respond. The DOL’s October 16, 2024 workplace-AI best-practices announcement likewise recommends transparency with workers and meaningful human oversight for significant employment decisions.
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Disclosure is only one control. It does not make an evaluation accurate, nondiscriminatory, or lawful by itself. The employer still has to apply performance standards consistently, check data quality, protect personal information, and assess the system’s effects.
Start by identifying what the system actually does
Do not write the notice from a vendor description or a general statement such as “AI may be used.” Map the real workflow, including any hidden features, integrations, and human actions.
Drafting and summarizing
A tool may organize a manager’s notes, summarize employee-supplied work, or draft narrative feedback. In this lower-impact pattern, the notice should say that the output is a draft or summary and that a manager must verify it against the underlying evidence. It should identify whether the system can introduce inferred themes or factual statements that were not written by the manager.
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A system that assigns a score, ranks employees, recommends a rating, flags “high potential,” or suggests disciplinary action has a more consequential role. Saying that AI merely “assists” is misleading if the recommendation normally determines the result or is rarely changed. The notice should describe the scoring or ranking function, the factors or data categories used in ordinary language, and the decision-maker’s actual discretion.
Monitoring and inferred information
Productivity logs, keystroke data, location records, communications metadata, call analytics, or inferred traits are different from documented work evidence supplied by the employee. Identify monitoring data separately and explain any material inferences, such as an estimated productivity pattern or sentiment label. Employees need to know when an apparent fact is a system-generated inference rather than an observed event.
Advisory output versus a materially determinative result
Ask what would happen if the AI output were removed. If the manager would make the same assessment using the underlying evidence, the tool may be advisory. If the score controls a threshold, automatically triggers a workflow, or is accepted in nearly every case, treat it as materially influential in the notice and in the employer’s risk review.
| System role | What the notice should make clear | Additional control |
|---|---|---|
| Organizes or summarizes information | Which records are summarized and that the summary can be incomplete or wrong | Manager checks the summary against source records |
| Drafts narrative feedback | That text is machine-generated and edited by a named role | Remove unsupported statements before delivery |
| Scores or ranks performance | The score or ranking function, inputs, review period, and influence on the outcome | Independent human review and a documented override process |
| Uses monitoring or inferred data | The monitoring categories, purpose, retention, and distinction between observations and inferences | Accuracy checks, proportionality review, and a way to challenge records |
| Automatically triggers a decision | The trigger, consequences, and whether a person can stop or change it | Escalation before an adverse action |
Eight elements of a clear employee notice
1. Purpose and review task
Name the particular task: organizing evidence for a quarterly review, drafting comments, identifying work against a documented goal, or recommending a rating. Avoid an open-ended statement that the employer uses AI “to improve performance management.” If the purpose changes, update the notice.
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The EEOC’s performance-evaluation guidance says employers should communicate performance standards, apply them consistently, explain whether employees met them, and provide relevant factual details that help employees understand the basis of a decision. The AI notice should connect directly to that existing evaluation process rather than create a separate, opaque scoring exercise.
2. Timing
Give the notice before the first AI-assisted review or decision involving the employee. Deliver it early enough for the employee to understand what will happen and raise questions; a link shown only after a rating is issued is an explanation after the fact, not advance notice.
For a newly introduced system, notify affected employees before data is collected for the review. For a changed system, notify them before the changed purpose, inputs, model, or workflow is used. Keep a record of the version of the notice in force during each review cycle.
3. Inputs in ordinary language
List categories, not just database names. For example, say “completed project milestones, quality checks, documented customer issues, and manager notes for the review period,” rather than “HRIS and productivity API data.” Distinguish:
- Evidence the employee supplied or that a manager documented.
- Monitoring information, such as system activity or location data.
- Information imported from colleagues, customers, or other systems.
- Model-generated labels, predictions, or inferences.
State the relevant review period and whether information from outside that period can be used. If the system uses a profile, benchmark, or historical rating, say so and explain its role.
4. The AI operation and its limits
Use verbs employees can understand: “summarizes,” “drafts,” “compares,” “scores,” “ranks,” “flags,” or “recommends.” Explain whether the system produces a draft for a manager, narrows a list, sets a threshold, or automatically sends a result into another process.
Describe known limitations without promising that a human will catch every error. A statement such as “AI output may be incomplete or inaccurate and is checked against the underlying records” is useful only if that check actually occurs. Do not call a system a writing aid when it assigns a score that controls eligibility for a promotion.
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5. Standards and factual basis
Identify the performance standards or goals used in the review and explain how evidence is matched to them. If a standard, weighting, target, or policy changed, explain the change and when it took effect. The EEOC specifically advises employers to explain whether employees met performance standards and to explain changed standards or policies.
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Give employees enough factual detail to understand a conclusion. A notice need not disclose proprietary model code, but it should not hide the practical basis of a rating behind the word “algorithm.”
6. Human accountability
Name the accountable role, such as the line manager with HR review, and describe what that person must do: verify source information, consider context the system cannot see, apply the communicated standards, document reasons for accepting or rejecting the output, and explain the final assessment.
Use “human review” precisely. A person clicking “approve” after seeing only a score is not meaningful oversight. If the system’s result effectively determines the outcome, say that plainly and create an escalation or stop point before an adverse action. The DOL recommendation for meaningful human oversight is a best-practice recommendation, not a universal statutory command, but it is a useful design standard.
7. Corrections, questions, and challenges
Provide a named team or role, a channel, and a response process. Employees should be able to:
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- Correct an inaccurate source record, such as a completion date or customer assignment.
- Ask what AI output was considered in their review.
- Point out missing context or an inappropriate inference.
- Request review by a person who was not responsible for the disputed data.
- Raise a concern about discrimination, retaliation, privacy, or a monitoring practice.
State whether a challenge pauses a rating, pay change, promotion decision, or disciplinary action, and give an expected response time if the employer has one. A generic HR inbox with no route for correcting the underlying record is not a practical remedy.
8. Privacy information
Where personal data is processed, point to the applicable privacy notice and summarize the items employees need for this use: purpose, categories of data, retention period or criteria, recipients, and any relevant rights or restrictions. The ICO says privacy information should cover processing purposes, retention periods, and who data is shared with. For information collected directly from individuals, its guidance says to provide the information when collected and before using it to train a model or apply the model to them.
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Explain whether review data is sent to an external processor, retained for model improvement, combined with data from other systems, or transferred outside the employee’s country. If a provider says it does not train on customer data, verify that the contract and configuration match the statement before putting it in the notice.
A practical process for employers
- Inventory the workflow. Record the tool name and version, owner, review stages, integrations, outputs, automated triggers, and every human who handles the result.
- Classify the decision impact. Mark whether the output is a draft, an advisory recommendation, a score or ranking, or an automatic trigger affecting pay, promotion, discipline, scheduling, or continued employment.
- Map the data. List source systems, monitoring streams, employee submissions, third-party reports, retention periods, recipients, and generated inferences. Identify data that is irrelevant or disproportionately intrusive and remove it.
- Test accuracy and context. Sample outputs against source records, check for missing work or mistaken attribution, and document how managers handle uncertainty. Do not rely on a polished narrative as proof that the input is correct.
- Connect the workflow to standards. Show which communicated goal or criterion each output informs. Explain any changed policy before applying it.
- Assign accountable reviewers. Define who verifies facts, considers employee context, approves the assessment, records an override, and answers questions. Train reviewers not to treat a model score as a conclusion.
- Write and deliver the notice. Use plain language, provide it before use, make it accessible, and retain the version delivered to each affected group.
- Operate the challenge route. Log corrections and questions, investigate the source data and output separately, correct downstream records, and tell the employee the result.
- Review changes. Reassess the notice and controls when the model, purpose, inputs, thresholds, provider, review policy, or retention practice changes.
Example notice language
The following is a starting point, not a legally validated template. Replace every bracket with facts about the actual system.
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[tool/system]to assist with[specific review task]. It may use[plain-language categories of information]to[summarize, draft, score, rank, or recommend]for your[review period]performance review. It does not replace the manager’s responsibility to check the source information, consider relevant context, and explain your assessment against[named performance standards or goals]. The output may be incomplete or inaccurate. You can ask what information and output were considered, request correction of an inaccurate record, or raise a concern with[person/team and contact route]. See[privacy notice]for the purpose, retention, recipients, and other information about processing your personal data. We will update this notice if the system, purpose, inputs, or review policy changes.
If a score or recommendation normally determines the result, change the wording to describe that influence. Do not promise that a manager makes the final decision when the workflow leaves no meaningful discretion.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Examples of notices matched to different systems
Drafted feedback from documented work
“The review platform summarizes completed milestones, quality records, and your manager’s notes, then drafts suggested comments. Your manager edits and verifies the draft against the team’s published goals. The draft is not itself a rating. Contact the review administrator to correct a source record or ask which draft content was used.”
This wording identifies the task, inputs, human check, and correction route without implying that the model evaluates qualities it was not designed to measure.
Scoring based on monitored activity
“The platform calculates a productivity score from recorded ticket completions, response times, and scheduled shift data for the stated review period. It does not treat keystroke counts as a measure of performance. The score is one input to the manager’s assessment and must be checked against work quality, assignments, leave, and documented circumstances. Ask HR for the score components or to challenge an inaccurate activity record.”
This example separates observed records from an inference about performance and tells the employee how the score fits into the decision.
Ranking that affects promotion eligibility
“The system ranks employees against the published promotion criteria using project results, required skills, and documented feedback. The ranking is shown to the promotion panel and can affect who is considered. Panel members must review the underlying evidence and record reasons for departing from or adopting the recommendation. Questions or corrections can be submitted to the panel coordinator before the decision date.”
Because the ranking has a material effect, the notice does not describe it as a minor administrative aid.
Common disclosure failures and fixes
| Failure | Why it misleads employees | Fix |
|---|---|---|
| “We use AI in HR.” | It does not identify the decision, data, timing, or consequence. | Name the task, inputs, output, review period, and affected process. |
| Notice delivered with the final rating | The employee cannot understand or respond before the decision. | Give advance notice and keep a dated record of delivery. |
| Calling a score “assistance” | It hides a ranking, threshold, or automated trigger. | Describe the score’s actual influence and any human discretion. |
| Listing only system names | Employees cannot tell what information the names represent. | Use ordinary-language data categories and distinguish monitoring from work evidence. |
| “A manager reviews everything” | The statement may be false if review is a rubber stamp. | Define verification, context review, override authority, and documented reasons. |
| No correction route | An employee may see an error but have no way to fix the record. | Provide a contact, channel, scope of challenges, and escalation path. |
| One permanent notice | System, purpose, data, and policy changes can make it inaccurate. | Version the notice and trigger a review for material changes. |
| Assuming disclosure cures bias | Transparency does not establish accuracy, consistency, or nondiscrimination. | Run separate accuracy, impact, and consistency checks. |
U.S. and UK guidance: what it does and does not establish
United States
The EEOC page “5. I’m conducting performance evaluations” is small-business guidance on performance evaluations and discrimination prevention. It is not an AI-specific disclosure regulation. Its practical points are to communicate standards, apply them consistently, explain whether employees met them, provide relevant factual details, and explain changed standards or policies.
The DOL’s October 16, 2024 announcement, “Department of Labor releases AI Best Practices roadmap for developers, employers, building on AI principles for worker well-being,” presents best-practice recommendations. Its transparency and meaningful-human-oversight recommendations support an employee notice and accountable review process; they should not be stated as a universal statutory mandate. Other federal, state, local, contractual, collective-bargaining, and sector requirements may apply.
United Kingdom
The ICO pages “The principles to follow,” “Data protection and monitoring workers,” and “How do we ensure transparency in AI?” address UK data protection, AI-enabled decisions, and workplace monitoring. They support advance awareness of a specific AI-enabled decision, meaningful explanations, information about purposes, retention and sharing, reasonable steps to prevent inaccurate or misleading monitoring records, and careful consideration of workers’ challenges. The ICO also says a data protection impact assessment is required before processing likely to create high risk under its guidance.
The ICO has flagged that parts of its guidance are under review following the Data (Use and Access) Act. Employers operating in the UK should check the live ICO material and obtain advice on the current requirements rather than treating this guidance as frozen text.
Questions to resolve before publishing a notice
- Does the system only draft or summarize, or does it score, rank, or trigger action?
- Can a manager genuinely change the result, and is that discretion used and recorded?
- Which inputs are direct work evidence, monitoring data, third-party reports, or model-generated inferences?
- What is the review period, and can older or unrelated data enter the calculation?
- What performance standards and changed policies does the output relate to?
- Who can correct a source record, investigate an output, and pause an adverse decision?
- How long are the data and outputs retained, who receives them, and are they used to train or improve a model?
- What jurisdiction, collective arrangement, contract, or sector rule changes the employer’s obligations?
A notice that answers these questions in plain language gives employees a realistic understanding of the review and a usable way to respond. The employer should then keep the underlying controls—consistent standards, accurate records, meaningful human accountability, privacy safeguards, and impact assessment—working alongside the disclosure.
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