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Tell employees before an AI-assisted review how the system will be used, what information it may affect, and which person remains accountable for checking the result. Give employees a practical way to question inaccurate data or AI output. The explanation should be plain-language, specific to the actual system, and delivered early enough for the employee to understand and respond.
There is no single global notice that makes an AI-assisted performance review lawful or fair. The useful baseline is operational: describe the system’s real role, identify the information it uses, connect its output to the performance standards employees were given, name the human decision-maker, and provide a route to correct errors. U.S. Equal Employment Opportunity Commission (EEOC) guidance addresses consistent, evidence-based performance evaluations; U.S. Department of Labor (DOL) recommendations call for transparency and meaningful human oversight; and UK Information Commissioner’s Office (ICO) guidance addresses transparency, personal-data use, monitoring, accuracy and impact assessment. Their scopes differ, so an employer should have local counsel or a privacy specialist check the final notice.
What the disclosure is supposed to do
A notice is useful when an employee can answer five questions without learning the system’s technical jargon:
- Why is the employer using AI in this review?
- What information goes into the system?
- What does the system produce: a summary, draft, score, ranking or recommendation?
- What will a manager independently verify, and who is accountable for the decision?
- How can the employee see, question or correct information that is wrong or misleading?
The ICO says organizations should make AI-enabled decision-making obvious and explain a specific AI-enabled decision in a meaningful way before it affects the person. Its wording is a useful standard for clarity: “Being transparent about AI-assisted decisions builds on these requirements. It is about making your use of AI for decision-making obvious and appropriately explaining the decisions you make to individuals in a meaningful way.” That does not mean every employee receives source code or a model formula. It does mean the notice must describe the practical effect of the system honestly.
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Map the system’s actual role before writing anything
Many weak notices call a tool an “assistant” even when it scores or ranks workers. Start with a written map of the workflow, then describe that workflow in the notice. The distinction matters because the more influence the system has over an employment outcome, the more detail and human review employees need.
| AI function | What to tell employees | Additional control to explain |
|---|---|---|
| Organizes or summarizes records | Identify the records and say that the output helps a manager find or summarize information. | Explain that the manager checks the summary against the underlying records. |
| Drafts feedback | Say that AI generates draft language from specified information. | State that the manager edits and verifies the draft before it is shared. |
| Scores or ranks performance | Disclose the scoring or ranking function, the categories of information used, and how the score relates to the stated standards. | Describe independent review, the possibility of correction, and whether the score can influence pay, promotion or discipline. |
| Recommends an employment action | Say that the system produces a recommendation and identify the action it may inform. | Explain who evaluates the recommendation and what evidence can override it. |
| Uses monitoring data or inferred traits | Name monitoring categories and distinguish observed records from system-generated inferences. | Explain accuracy checks, limits on use and how an employee can challenge an inference. |
Do not describe a system as merely drafting feedback if it determines a rating, filters evidence or automatically triggers a consequence. If the result effectively determines the outcome, a sentence saying “your manager makes the final decision” may be misleading unless the manager genuinely reviews and can change the result.
Give notice at a time employees can use
Provide the notice before the AI-assisted review or decision, not for the first time in the delivered rating. Advance notice lets an employee understand what information will matter, point out an incorrect record and ask questions while the review can still be corrected. The ICO also says that when information is collected directly from people, privacy information should be provided when it is collected and before it is used to train a model or apply the model to them.
Use a channel employees actually receive and can revisit: a policy page linked from the performance-review system, an onboarding or manager briefing, and a review-cycle reminder. Make the AI section prominent instead of burying it in a general privacy policy. If the system, purpose, inputs or performance policy changes, issue an updated notice before the changed use begins and keep a dated version history.
What a useful notice should cover
Purpose and review task
State the concrete task, such as organizing evidence, summarizing goals, drafting comments or generating a suggested score. Avoid broad language such as “we use AI to improve HR.” An employee should know whether the tool affects an annual appraisal, a probation review, promotion screening, a bonus recommendation or another specific process.
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Categories of information used
List categories in ordinary language. Examples include documented goals, completed work, quality or service records, attendance information where permitted, manager comments, peer feedback, training records, or monitoring information. Distinguish employee-supplied or documented evidence from data collected through monitoring and from inferences generated by the system. If the tool uses data from another system, identify that source category and the period covered.
Do not imply that the model sees only factual work evidence if it also processes free-text comments, communications metadata or behavioral signals. If a category is excluded, say so only when the employer can enforce that limitation.
What the AI does and does not do
Use verbs employees can understand: “summarizes,” “drafts,” “scores,” “ranks” or “recommends.” State whether the output is advisory or can materially determine a rating or action. Explain important limits, such as the system’s inability to verify context, recognize an approved accommodation or resolve conflicting records without human review.
Performance standards and factual basis
Connect the AI-assisted step to the standards already communicated to employees. EEOC employer guidance says to communicate performance standards, apply them consistently, explain whether employees met them, and include relevant factual details that help employees understand the basis of a decision. It also says to explain changed standards or policies. The agency’s wording is direct: “Explain whether employees have met performance standards.”
A notice cannot substitute for clear standards. Tell employees where the applicable goals, competencies or policies are located and explain how factual evidence is used to assess them. If the employer changes a standard, explain the change and when it applies rather than allowing an AI-generated comparison to silently impose a new measure.
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Human accountability and review
Name the role accountable for checking the output, such as the employee’s manager with HR oversight, and describe the checks that person performs. The DOL’s October 16, 2024 workplace AI best-practices announcement recommends meaningful human oversight for significant employment decisions and transparency about how AI may assist workers. A meaningful review is more than clicking “approve”: the reviewer should examine source information, consider context, correct obvious errors and be able to reject or change the output.
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Correction, questions and challenge
Give employees a named contact or team, a usable channel and a response process. Invite challenges to inaccurate source records, misleading summaries, missing context and inappropriate inferences. Explain whether the employee can submit documents or comments and whether the review will be paused while a material error is checked.
For monitoring information used in reviews, ICO guidance calls for reasonable steps to prevent inaccurate or misleading records and careful consideration of workers’ challenges. A challenge route is therefore part of the control design, not a courtesy added after deployment.
Privacy information
Where personal data is processed, point to the applicable privacy information and summarize the essentials in the notice: the purposes, retention period or criteria, and who the data is shared with. Explain whether data is used to train a model, whether a vendor processes it, and whether information from the review is reused for another HR purpose, if those facts apply. Keep the AI notice consistent with the employer’s privacy notice; conflicting descriptions undermine trust and can create compliance problems.
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Changes and versioning
State how employees will be told about material changes. A new data source, a move from drafting to scoring, a new vendor, a different retention period or a changed performance standard should trigger review of the notice. Date the notice and identify the covered review cycle so employees can tell which explanation applied to their assessment.
Connect the disclosure to the review itself
An AI notice works best as part of a documented review process:
- Publish the standards. Give employees the goals, competencies, rating definitions and relevant policy before the period being assessed.
- Describe the AI step. Before running the review, identify the tool, its task, the data categories and whether the output is a draft, score or recommendation.
- Check source records. The manager or designated reviewer verifies that the records are about the right employee, cover the right period and are not obviously incomplete or misleading.
- Review the output critically. Compare the AI output with source evidence and the published standards. Correct unsupported statements, omissions and inappropriate comparisons.
- Explain the assessment. Provide factual details showing how the employee met or did not meet each relevant standard. Do not present an unexplained model score as the reason.
- Offer a correction route. Tell the employee where to challenge data or output, what information to provide and who will respond.
- Record the outcome. Retain the notice version, relevant evidence, material corrections and the human decision in accordance with the employer’s retention policy.
Monitoring data needs extra care
Monitoring can include activity logs, location signals, software-use records, communications metadata, call statistics or other measurements. Such data may be incomplete, collected for a different purpose or unable to explain why work took longer. Inferences can add another layer of uncertainty: a system may infer engagement, reliability or sentiment from patterns that are not direct observations.
Tell employees which monitoring categories are used and what they are not used for. Explain the period covered and any material exclusions. Before relying on a record, check for data-quality problems such as duplicate events, missing shifts, shared accounts, approved leave, accommodations, equipment failures or work performed outside the monitored system. Give the employee a way to provide context and ask for correction.
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Jurisdiction: what the cited guidance does and does not establish
| Source and jurisdiction | What it supports | Important limit |
|---|---|---|
| EEOC, United States | Communicate standards, apply them consistently, explain whether employees met them, provide relevant factual details, and explain changed standards or policies. | The page is federal small-business guidance on performance evaluations and discrimination prevention, not an AI-specific disclosure rule. |
| DOL, United States, October 16, 2024 | Best-practice recommendations for transparency with workers and meaningful human oversight in significant employment decisions. | The announcement describes recommendations, not a universal statutory mandate for every employer or system. |
| ICO, United Kingdom | Be open about AI-enabled decisions, explain a specific decision meaningfully, provide privacy information about purposes, retention and recipients, and address accuracy and worker challenges in monitoring. | The guidance concerns UK data protection and employment monitoring. The ICO says some guidance is under review following the Data (Use and Access) Act; check the live guidance and obtain current local advice. |
Employer location, the AI system’s design, data sources, the decision’s effect, employee notice, collective arrangements and workplace policies can change the analysis. These sources support good disclosure practice but do not resolve every employer’s duties. Disclosure also does not by itself make a system accurate, nondiscriminatory or lawful; consistency, data quality, accountability and impact assessment remain separate responsibilities.
A practical approval workflow for employers
- Inventory the use. Document every AI feature in the review process, including hidden vendor settings, imported data and automated alerts.
- Classify the consequence. Mark whether the output is informational, advisory, a score or ranking, or a factor that can materially determine pay, promotion, discipline or termination.
- Validate the data map. Confirm each input category, source, period, access group, retention rule and any model-training use.
- Draft in employee language. Replace technical labels with concrete descriptions and examples of what the system can and cannot do.
- Assign accountability. Name the reviewer, escalation route and correction owner. Confirm that the reviewer has authority to change the output.
- Test the correction path. Submit a sample inaccurate record and verify that it can be found, corrected and excluded or updated in the review.
- Check jurisdiction and workplace arrangements. Review applicable U.S. or UK requirements, contracts, collective agreements, works-council duties and internal policies before rollout.
- Publish and update. Deliver the notice before use, retain the version and notify employees before material changes take effect.
Example employee notice
We use
[tool/system]to assist with[specific review task]. It may use[plain-language categories of information]to[summarize/draft/score/recommend]. Your manager is responsible for reviewing the information and explaining your assessment against the performance standards. AI output may be incomplete or inaccurate. You can ask how it affected your review, provide context, or request correction by contacting[person/team]at[contact]. See[privacy notice]for information about the purpose, retention and sharing of your personal data.
Adapt every field to the actual workflow. Add the consequence the output may inform, the review period, monitoring categories and any escalation or appeal step that employees need to use. Do not say that a manager makes the final decision if the system’s result effectively determines it.
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Common disclosure failures and repairs
- “We use AI in HR.” Repair: name the review task and output type.
- Calling a score a draft. Repair: disclose scoring or ranking and its influence on the decision.
- Listing “employee data” as the only input. Repair: identify categories and distinguish records, monitoring and inferences.
- Giving notice after the rating. Repair: provide it before collection, model use and the AI-assisted review where applicable.
- Relying on a human sign-off box. Repair: require source checking, context review and authority to change the output.
- Offering no correction route. Repair: provide a contact, evidence-submission method and response process.
- Assuming disclosure cures bias or inaccuracy. Repair: test data quality, consistency, impact and outcomes separately.
- Leaving an old notice online after a system change. Repair: version the notice and update it before the new purpose, input or function is used.
What employees should reasonably expect to learn
A complete notice should let an employee identify the AI-assisted step, understand the categories of information involved, see how the result connects to stated standards, know which person is accountable, and use a real process to challenge errors. If the employer cannot explain those points plainly, it is not ready to describe the system as a limited assistant or to rely on the output in a consequential review.
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