Yes, you can challenge an AI-assisted performance review, but there is no universal right to an appeal or to inspect an employer’s model. The strongest practical response is a prompt, written challenge that identifies each factual error or missing context, supports it with dated work evidence, asks how the software affected the rating, and requests human reconsideration before the review is used for pay, promotion, discipline, or continued employment. A legal right to disclosure, correction, contest, or a remedy depends on your jurisdiction, the tool’s role, the decision’s consequences, and whether the issue involves disability accommodation, discrimination, privacy, or simple accuracy.
This guide separates a prudent workplace request—such as asking a manager to explain a score—from an entitlement created by a particular law. It is general information, not individualized legal advice.
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What to do first when a review appears to use AI
- Preserve the review. Save the exact version you received, the date and time, rating, stated criteria, goals, and any notice saying that artificial intelligence, automated decision-making, analytics, or another software tool was used. Keep the delivery email or system notification. Do not alter the original file.
- Record the consequence. Note whether the rating is already being used—or is expected to be used—for compensation, promotion, performance improvement plans, discipline, termination, scheduling, eligibility, or another employment decision. The more significant the consequence, the more promptly you should escalate a documented concern.
- Make a fact list. Copy each disputed statement or score into a separate line. Beside it, write the relevant date, goal, metric, deliverable, or event and the short correction you seek. Separate a demonstrably wrong input from a disagreement with a manager’s judgment.
- Use only records you may properly access. Gather agreed objectives, completed work, measurable outcomes, prior feedback, approved communications, and context such as changed priorities, leave, outages, resource limits, or an accommodation. Follow confidentiality, trade-secret, privacy, and company-record rules; do not forward restricted customer or colleague data to a personal account.
- Send a calm written response. Ask that it be attached to the performance record and request human reconsideration before the rating is used for a consequential decision. Keep a copy in an authorized system and record who received it.
An internal response does not necessarily stop an external filing deadline. If the consequence is serious, confirm deadlines with the relevant agency or qualified employment counsel rather than waiting for an internal appeal.
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How to find out whether AI affected your rating
Employers may use software to summarize feedback, identify patterns, score goals, flag language, rank employees, or recommend a rating while a manager makes the final decision. “AI-assisted” therefore covers very different processes. Ask for facts about the particular review instead of assuming that the software made the decision.
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Questions about the tool and data
- Was artificial intelligence or another automated tool used to prepare, score, summarize, rank, or materially influence this review?
- What is the tool’s name, version or function, and which parts of my work or data were evaluated?
- What time period, targets, facts, comparison group, and data sources informed the rating?
- Was the output a score, classification, recommendation, summary, or flag? How much weight did it receive?
- Were missing, stale, duplicated, or incorrect records used?
Questions about human judgment and correction
- Who reviewed the output and who made the final decision?
- Can a human reconsider the rating without relying on the original output?
- Can I see or correct information about me that was used?
- How can I attach a written response to the performance record?
- What process applies if I need an accommodation for the evaluation?
These are useful prompts, not a promise that every employer must answer every question. A general United States disclosure right to a model’s source code, internal prompt, or complete training data is not established here. A specific law, collective-bargaining agreement, employment contract, company policy, or local rule may provide more.
How to build a persuasive written challenge
Lead with the requested correction
State that you are responding to the review, identify the rating and date, and say exactly what you want changed or added. A focused request is easier to evaluate than a broad assertion that an algorithm was unfair.
Use one entry for each disputed point
For every error, quote or describe the statement, identify the relevant period, provide the supporting record, and explain the impact. For example, “The review says the project was delivered after the agreed date. The approved change on 14 May moved the deadline to 30 June; the final deliverable was submitted on 28 June.” If the issue is context rather than a false statement, identify the context and why it changes the interpretation.
Distinguish facts from judgment
A time stamp, approved target, completed deliverable, or documented outage can support a factual correction. A conclusion such as “the work was not strategic enough” may be managerial judgment. You can ask how that judgment was reached and whether the same criteria were applied consistently, but do not present a preference as a provable data error.
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Ask for a human review before a decision is made
Request that a named manager, HR reviewer, or other authorized person reconsider the disputed points and confirm the outcome in writing. Ask that your response and any correction be retained with the original review, even if the rating does not change.
Keep the tone professional and narrow
Do not accuse an employer of unlawful discrimination solely because software was involved. Identify the evidence, explain the consequence, and reserve legal conclusions for a process that can assess them. A factual record is useful whether the outcome is a corrected rating, an accommodation, an agency complaint, or later legal advice.
A compact response format
Subject: Written response to performance review dated {{review_date}}
I am requesting correction and human reconsideration of the review issued on {{review_date}}. Please attach this response to the performance record and consider it before using the rating for {{pay/promotion/discipline/other}}.
1. Review statement or score: {{quote_or_description}}
Correction requested: {{specific_change}}
Supporting record and dates: {{evidence}}
2. Review statement or score: {{quote_or_description}}
Correction requested: {{specific_change}}
Supporting record and dates: {{evidence}}
Please confirm whether automated software prepared, scored, summarized, or materially influenced this review; what information and period it evaluated; how much weight its output received; who made the final decision; and how I may correct information about me. If an accommodation is relevant to the evaluation process, please tell me whom to contact.
Please confirm receipt and the result of the human reconsideration.What to document and how to organize it
- The review record: original file or screen capture, date received, rating, criteria, goals, manager comments, and any automated-assessment notice.
- Each disputed item: exact wording or score, why it is inaccurate or incomplete, correction sought, and the date or period involved.
- Work evidence: agreed objectives, completed deliverables, measurable outcomes, approved changes, relevant feedback, and communications you may lawfully retain.
- Material context: changed goals, workload or staffing changes, system outages, resource constraints, leave, an accommodation, or other events that affect interpretation.
- Process history: dates, participants, questions asked, answers received, requests for correction, and whether a human reviewer reconsidered the result.
- Impact: notice of a pay, promotion, discipline, improvement-plan, termination, or other decision tied to the rating.
Keep a chronological log with the date, sender or recipient, communication method, and a short description. Store copies through authorized systems and preserve metadata where possible. Do not secretly record conversations or remove company information unless applicable law and workplace policy permit it.
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When disability, accommodation, or discrimination may be involved
The U.S. Equal Employment Opportunity Commission and Department of Justice have explained that employers use artificial intelligence and other software to monitor performance and make decisions about pay or promotion. Their guidance identifies several disability-related risks:
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- the evaluation process may fail to provide a reasonable accommodation;
- a tool may screen out a person with a disability who could perform the job with or without accommodation; or
- software may trigger impermissible disability-related inquiries or medical examinations.
An inaccurate rating alone does not establish an ADA violation, and software involvement does not automatically make a review unlawful. If a disability, accommodation request, medical restriction, or known impairment may have affected the result, state the concern factually, identify the accommodation needed, and use the employer’s designated accommodation channel. Keep proof of when you requested help and how the process responded.
EEOC Chair Charlotte A. Burrows said: “New technologies should not become new ways to discriminate. If employers are aware of the ways AI and other technologies can discriminate against persons with disabilities, they can take steps to prevent it.” That statement addresses disability risks; it is not a ruling that every AI-assisted review violates the ADA.
New York City: when Local Law 144 may matter
New York City’s Local Law 144 regulates certain automated employment decision tools (AEDTs). The city’s definition covers computational processes whose score, classification, or recommendation substantially assists or replaces discretionary decisions. Covered employers and agencies must satisfy audit, public-summary, and notice conditions before covered use. The city describes the law as applying to screening employees for promotion as well as job candidates.
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A performance review may fall outside Local Law 144 if the tool only assists a manager or is not used to screen for promotion. Coverage depends on the tool’s legal definition and how the employer uses it. Ask what role the output played and whether the rating was used to select people for promotion or another covered employment action.
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Information and complaint routes
NYC rules provide that when required information about data type, data source, and retention is not posted, it must be available upon a written request from a candidate or employee. Ask the employer where the required notice, data-source and retention information, and audit summary can be found.
NYC311 says employees can report suspected failures to complete the required audit, post the audit summary, or give required notices. A complaint should identify the suspected violation date, explain what happened, provide position details, name or describe the AEDT if known, and include any notice received. A complaint route is not a guarantee that an investigation will occur or that an individual will receive a remedy.
Current enforcement context
A New York State Office of the State Comptroller audit issued 2 December 2025, covering July 2023 through June 2025, reported that the Department of Consumer and Worker Protection (DCWP) could impose civil penalties of $500 to $1,500 per day for violations. The audit reported two AEDT complaints during the period it reviewed. Its auditors reviewed 32 company websites and identified at least 17 potential non-compliance instances; the audit also said DCWP’s own survey identified one issue and noted methodological limitations. These are audit findings and reported statutory penalties, not a prediction of an individual complaint’s outcome.
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Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.European Union: GDPR Article 22 and automated decisions
Article 22 of the General Data Protection Regulation addresses a decision based solely on automated processing, including profiling, that produces legal effects or similarly significantly affects a person. It is not a blanket rule for every review that contains an AI-generated summary or recommendation.
The regulation includes exceptions involving contract necessity, authorization by law with safeguards, or explicit consent. In the contract and consent situations, safeguards include the right to obtain human intervention, express a point of view, and contest the decision. Whether a performance review qualifies depends on the degree of automation, the significance of the effect, the legal basis, and applicable national employment rules. A manager who meaningfully evaluates the evidence may mean the decision is not “solely automated,” although the specific facts and local law control.
How the main frameworks differ
| Framework | When it may apply | What an employee can seek | Important limit |
|---|---|---|---|
| U.S. disability protections under the ADA | Software is used to monitor performance or influence pay or promotion, and disability, accommodation, screening, or medical-inquiry issues are present. | Accommodation, a nondiscriminatory evaluation process, and a route to raise disability-related concerns with the employer or EEOC. | An inaccurate AI-assisted review is not automatically an ADA violation; the guidance is disability-specific. |
| New York City Local Law 144 | A covered AEDT substantially assists or replaces discretionary decisions, including certain screening for promotion, and the employer has notice, audit, or public-summary obligations. | Required notices and information; a report to NYC311 for suspected audit, summary, or notice violations. | A routine human-led performance review is not automatically covered, and a complaint does not guarantee a personal remedy. |
| EU GDPR Article 22 | A decision is based solely on automated processing and has legal or similarly significant effects, subject to the regulation’s exceptions and safeguards. | In qualifying cases, human intervention, an opportunity to express a point of view, and a chance to contest the decision. | Article 22 does not apply to every AI-assisted review, particularly where a manager exercises meaningful judgment; legal basis and national employment rules matter. |
When to escalate beyond the manager
Use HR or the designated review process
Escalate when the manager does not correct a documented factual error, refuses to attach your response, cannot explain the evaluation process, or is about to rely on the rating for a serious employment action. Follow any written review, grievance, union, or collective-bargaining procedure and keep proof of submission.
Consider an agency or legal consultation
Consider prompt advice when the issue involves disability accommodation, discrimination, retaliation, a promotion or pay decision, discipline, termination, a covered New York City AEDT, or a potentially solely automated decision in the European Union. Bring the review, your chronology, evidence, communications, and the consequence you are trying to prevent. Ask specifically about filing deadlines; the materials available here do not provide a complete deadline map for every discrimination, labor, contract, or data-protection claim.
Do not assume an internal process pauses a deadline
An employer’s promise to “look into it” may not extend a statutory filing period. Confirm the deadline and filing method with the responsible agency or qualified counsel while the internal review proceeds.
Common mistakes that weaken a challenge
- Waiting until after the consequence. A correction request is easier to act on before a promotion, pay, discipline, or termination decision is finalized.
- Arguing about AI in the abstract. Identify the disputed input, score, criterion, or human decision instead of asserting that the presence of software proves bias.
- Demanding information as if it were universal. Ask for the tool, data, weight, reviewer, and correction process; describe legal rights only where a specific rule applies.
- Sending an unfiltered data dump. Attach concise, relevant evidence and explain each item’s significance. Protect confidential and personal information.
- Conflating disagreement with error. Label factual corrections separately from challenges to managerial judgment or inconsistent application of criteria.
- Relying on a bias audit as a personal verdict. An audit or public summary does not prove that an individual rating was fair or unfair.
- Assuming a manager’s involvement ends all legal questions. Human participation may matter under GDPR Article 22, but disability, discrimination, notice, data, and accommodation issues can still require analysis.
A practical decision path
- Is there a specific factual error? Document it with dates and work evidence, request correction, and ask that your response be attached.
- Was automated software materially involved? Ask what it did, what data it used, how much weight it received, and who made the final decision.
- Will the rating affect a significant employment outcome? Request human reconsideration before the decision and record the expected impact.
- Is disability, accommodation, or unequal treatment implicated? Use the accommodation or discrimination channel and preserve the timeline.
- Are you in New York City or the European Union? Check the conditional Local Law 144 or GDPR Article 22 criteria rather than assuming they apply.
- Could an external deadline run? Verify it immediately; do not rely on an internal appeal to pause it.
The goal is a clear record: what was said, what is wrong or incomplete, what evidence supports the correction, how software affected the process, who exercised judgment, and what remedy you requested. That record gives a manager, HR reviewer, agency, union representative, or lawyer something concrete to assess without requiring you to prove that every automated system is unlawful.
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