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Look for two things: what workplace data is being collected and whether an automated score or recommendation affects a decision about your job. Time-per-task metrics, GPS, keystroke logs, screenshots, customer ratings, voice or facial analysis, and automated productivity flags can all be clues. None, by itself, proves that an employer is using artificial intelligence; ordinary software can collect the same information. Verification requires checking the employer’s policies and asking how the system works, who reviews its output, and how you can challenge an error.
What “AI evaluation” can mean at work
Employers may use software to observe work, summarize performance, assign a score, or recommend an action. “AI” is often used loosely, so start by separating the system’s functions rather than relying on its label.
| What to examine | Examples of clues | What the clue does—and does not—establish |
|---|---|---|
| Data collected | Time spent on tasks, GPS or other location data, keystrokes, cursor movement, screenshots, email, webcam images, audio, facial or voice cues, wearable-sensor data, or customer feedback | Shows monitoring or measurement. It does not prove that an AI model analyzes the data. |
| Output produced | A productivity score, risk flag, ranking, recommendation, automated schedule, or suggested training | Shows automation may be involved. A score can be calculated by a fixed rule or conventional analytics rather than AI. |
| Effect on employment | Training assignments, productivity instructions, pay increases, promotion, discipline, layoff selection, or termination | A connection to a consequential decision is more significant than the presence of a dashboard alone. |
| Human involvement | A manager reviews the result, accepts it automatically, or can override it | Explains how much influence the system has and whether a person can catch an error. |
The Equal Employment Opportunity Commission (EEOC) and other federal agencies describe these as examples of workplace technology, not a diagnostic checklist. An employer could use one or more of them without calling the system AI, while a system marketed as AI might use very little direct monitoring.
Strong clues that merit a closer look
Monitoring that goes beyond ordinary timekeeping
Read the wording of monitoring notices and software instructions. Terms such as “activity level,” “behavioral analytics,” “automated quality assessment,” “productivity intelligence,” “sentiment,” or “risk score” suggest that collected data may be processed into an evaluation. Specific references to keystrokes, screenshots, cursor activity, webcam photos, audio recording, GPS, RFID badges, or wearable devices are also worth recording. The National Labor Relations Board General Counsel’s October 31, 2022 memorandum lists these technologies as examples of electronic surveillance and automated management practices.
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Monitoring alone is not proof of an AI decision. A GPS application may simply record a route, and a time clock may total hours with no predictive model. Ask what happens after the data is collected.
Scores, rankings, and recommendations that change your treatment
Look for a new number or category attached to your work: a quality score, “adherence” percentage, attendance risk, customer-sentiment rating, or automated ranking. Find out whether that output triggers coaching, mandatory training, a change in assignments, a bonus decision, a promotion review, discipline, or selection for layoff. The closer the link between the output and a high-stakes decision, the more important it is to identify the data and decision-maker behind it.
Language suggesting automated decisions
Policies may say that a tool “recommends,” “flags,” “prioritizes,” “screens,” “predicts,” or “determines eligibility.” Those words do not have one uniform legal meaning, but they tell you to ask whether a person independently evaluates the result or merely approves it.
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A sudden requirement to install monitoring software, wear a sensor, keep a webcam on, or meet a newly calculated target is a practical reason to request an explanation. A surprising warning or employment decision is a reason to investigate—not proof that AI was used or that the employer broke the law.
How to verify what system is being used
Use a methodical sequence so that you distinguish documented facts from assumptions.
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- Collect the employer’s written materials. Check the employee handbook, privacy notice, electronic-communications policy, monitoring policy, performance-dashboard instructions, onboarding documents, and messages announcing a new tool. Save copies of materials you are entitled to access, noting the date and version.
- Identify the system and its owner. Record the product or internal system name, the department that administers it, and whether a contractor or staffing agency operates it. A screenshot of a dashboard label or a policy paragraph can be useful later.
- List the inputs. Ask which data fields are used: hours, location, keystrokes, screenshots, email, call recordings, customer surveys, webcam or microphone signals, wearable data, or information imported from another system. Ask whether the system uses historical performance data or data about other workers.
- Ask what the system produces. Determine whether it creates a score, ranking, prediction, alert, recommendation, or only a report. Ask how often it runs and whether managers see raw data as well as the calculated output.
- Trace the decision path. Ask which actions can follow the output—training, scheduling, pay, promotion, discipline, layoff, or termination—and who makes the final decision. “Advisory” is meaningful only if a human actually reviews and can reject the recommendation.
- Find the correction and review route. Ask how to inspect the underlying information, correct an inaccurate record, request a human review, appeal a score, or report an inaccessible assessment. Request the process in writing when possible.
These questions are practical transparency steps. Federal agencies encourage worker input and meaningful human oversight, but the materials discussed here do not establish a universal U.S. requirement that every employer disclose every technical detail or answer every question.
Questions to ask HR or your manager
| Question | Why it matters |
|---|---|
| What system or vendor is involved, and what is its purpose? | Names the process instead of leaving you to infer it from a score or interface. |
| Does it use automated scoring, ranking, prediction, or recommendations? | Separates simple recordkeeping from an evaluative model. |
| What information is collected, for how long, and from which devices or accounts? | Defines the evidence on which the evaluation rests. |
| Does the system analyze audio, video, facial or voice cues, email, location, keystrokes, screenshots, or customer feedback? | Surfaces less obvious inputs that may affect a result. |
| Which employment decisions can the output influence? | Shows whether the tool has consequences beyond coaching or reporting. |
| Who reviews the output, and can that person override it? | Clarifies whether a human exercises independent judgment. |
| How can I see and correct the data or score? | Provides a way to fix a bad input before it affects a decision. |
| What is the appeal, accommodation, or human-review process? | Identifies the route to address an error, disability-related barrier, or inaccessible assessment. |
Keep the tone factual. You are trying to establish how a process operates, not asserting that a particular technology is unlawful.
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How to tell ordinary automation from AI
Workplace technology exists on a spectrum. A spreadsheet formula that totals sales, a badge reader that records entry, and a scheduling rule that assigns shifts are automated, but they may not involve machine learning or generative AI. Conversely, a system can use statistical or machine-learning methods without displaying a label that says “AI.”
Ask for the function and decision path rather than arguing over terminology:
- Recordkeeping: The tool stores an event, such as a clock-in or delivery location.
- Rule-based calculation: A fixed rule converts events into a total or threshold.
- Automated analysis: Software detects patterns, classifies behavior, or predicts an outcome.
- Recommendation: The system proposes training, scheduling, discipline, or another action.
- Decision influence: A manager relies on the output in a consequential employment decision.
Only the employer’s documentation or an informed explanation can establish which category applies. A dashboard, an algorithmic-sounding label, or a poor decision is not conclusive evidence by itself.
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When an automated result affects your job
Performance management
Ask whether a productivity target or quality score is generated from complete and accurate data. For example, a customer-rating system may fail to account for language, assignment mix, accessibility needs, equipment failures, or work that is not captured digitally. Note the date, the score, the stated target, and the action taken.
Pay, promotion, or training
Determine whether the output is one factor among several or a gate that automatically disqualifies you. Ask whether decision-makers can consider context that the system cannot see, and whether the same criteria are applied consistently to comparable workers.
Discipline, layoff, or termination
Request the policy or notice identifying the reason for the action. Ask whether an automated flag initiated the review, supplied evidence, or made a recommendation. Do not assume that an employer must reveal proprietary source code; focus on the data used, the role of the output, the human decision-maker, and the correction process.
Your U.S. federal protections
Federal employment-discrimination laws continue to apply when an employer uses AI or other automated technology. The EEOC’s worker fact sheet, issued April 29, 2024, identifies protection against discrimination based on race, color, religion, sex (including gender, sexual orientation, and pregnancy), national origin, age 40 or older, disability, and genetic information.
Disability, pregnancy, and religious accommodation
An automated assessment may create a problem if it screens out a qualified person with a disability, fails to provide an accommodation, or prompts disability-related questions or medical-information requests. The EEOC and U.S. Department of Justice warned about these risks on May 12, 2022. Existing law may require a reasonable accommodation for a disability, religious practice, or pregnancy-related limitation, depending on the circumstances.
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If a test, video interview, speech analysis, typing metric, or attendance system does not account for a documented limitation, ask HR about an accommodation and an alternative evaluation method. Explain the work-related barrier without volunteering unnecessary medical details, and follow the employer’s established accommodation process.
Disparate treatment and disparate impact
The EEOC’s guidance on employment tests and selection procedures explains that a test can raise federal discrimination concerns if it intentionally discriminates or disproportionately excludes a protected group without the required legal justification. A statistical disparity alone does not resolve a case, but it is a reason to preserve records and seek advice rather than assuming the tool is neutral.
Surveillance and labor rights
The NLRB General Counsel’s October 31, 2022 announcement discussed wearable devices, cameras, RFID badges, GPS, keyloggers, screenshots, webcam photos, and audio recording. It described an intended argument that certain surveillance and automated-management practices could interfere with employees’ rights under Section 7 of the National Labor Relations Act, including in specified circumstances where disclosure was discussed. That announcement described the General Counsel’s proposed enforcement position; it is not a blanket federal rule requiring every employer to disclose AI evaluation to every worker.
Responsible-use practices
The Department of Labor’s October 16, 2024 AI Best Practices announcement emphasizes transparency, meaningful human oversight, worker input, rights, and data security. It describes the practices as a roadmap for responsible use, not as a single nationwide employee-notice statute.
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What to do if the process seems unfair or inaccessible
- Preserve the record. Keep the relevant policy, notice, dashboard entry, score, warning, schedule, decision letter, and messages. Record dates, participants, and what you were told.
- Request clarification in writing. Ask what system produced the result, which data it used, whether a person reviewed it, and how to correct an error.
- Use the accommodation process when appropriate. If a disability, religious practice, or pregnancy-related limitation affects the assessment, request a reasonable accommodation or alternative evaluation promptly.
- Use internal review channels. Follow the stated appeal, grievance, ethics, HR, or manager-review process. A union-represented worker can contact a union representative.
- Contact the EEOC about suspected discrimination. The EEOC directs workers who believe AI or another automated technology caused discrimination to its Public Portal or the phone contacts listed in its worker fact sheet.
- Get location-specific advice. State and local privacy, wage, labor, and notice rules may provide additional rights or procedures. A qualified employment lawyer, worker center, or relevant agency can assess the facts and deadlines.
Document facts without copying confidential information you are not authorized to possess. Do not bypass security controls or secretly record conversations where local law prohibits it.
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A practical evidence checklist
- The name, version, or description of the monitoring or evaluation system
- The handbook, privacy notice, monitoring policy, and tool instructions in effect when the event occurred
- Dates and times of data collection, warnings, meetings, and employment decisions
- Scores, rankings, flags, screenshots, or dashboard entries you are permitted to retain
- Messages explaining targets, model-generated recommendations, or the reason for an action
- Your requests for correction, accommodation, appeal, or human review and the responses
- Comparable examples showing how similar situations were handled, if lawfully available
- Names and roles of people who reviewed or approved the result
This record helps separate an incorrect input from a flawed evaluation method and a flawed method from unlawful discrimination. It also gives an agency, union representative, or lawyer something concrete to assess.
Questions that cannot be answered from federal guidance alone
There is no single federal rule in the sources discussed here that guarantees every worker advance notice of every AI system, access to an employer’s model, or a universal right to human review. Public-sector employees, contractors, applicants, union members, and workers outside the United States may have different rules. State and local laws can address biometric data, electronic monitoring, automated decision systems, wage records, or notice requirements in different ways.
Use the employer’s written policies and current official guidance for your jurisdiction. Treat an agency announcement, best-practice document, or General Counsel proposal according to its actual legal status rather than as a nationwide mandate.
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Bottom line
The most reliable way to tell whether AI is evaluating your work is to trace the chain from data collected to automated output to employment consequence. Monitoring is not proof of AI, and AI use is not automatically illegal. Ask precise questions, preserve the documents and scores you can lawfully access, request correction or accommodation when needed, and use the EEOC or other appropriate channels if an automated process appears to have caused discrimination.
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