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To audit whether an AI salary tool gives different advice when demographic cues change, compare repeated, matched prompts that differ only in the cue being tested. Keep the job and qualifications fixed, record the model version and prompt perspective, and compare both the recommended pay and the advice around it. This tests the tool in a defined scenario; it does not, by itself, prove discrimination in an employer’s actual payroll or establish why a difference occurred.
What exactly are you auditing?
Start by identifying the decision the AI influences. A tool that advises a candidate on a target salary is different from an employer-facing system that recommends an offer, and both differ from an AI agent that negotiates on someone’s behalf. An audit of advice measures what the system recommends under test conditions; it does not automatically measure what an employer ultimately pays.
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Specify the output you will evaluate: an opening offer, a target salary, bargaining tactics, a response to a counteroffer, or a final compensation package. Also record the intended user, geography, occupation and market context. These details define what a result can reasonably say.
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Does AI give women lower salary advice?
A 2025 peer-reviewed PLOS ONE study tested salary negotiation advice in a simulated US technology job market. Researchers submitted 98,800 prompts to each of four ChatGPT versions, varying gender, university and major, and using prompts voiced from either the employee’s or the employer’s perspective. They reported statistically significant gender-associated differences in recommended offers for all four tested models, but those gaps were smaller than differences associated with some other tested attributes. Differences between model versions and prompt perspectives were larger.
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This is evidence about those versions, prompts and simulated scenario—not a finding about every AI agent, every occupation, actual pay outcomes or demographic cues the study did not test. The authors emphasize the contextual nature of the experiment; its results do not certify models as generally biased or unbiased.
How to run a matched-prompt audit
- Set the scope. Document whether the AI advises a worker, advises an employer or negotiates for one side. Define the job, location, experience, qualifications, requested output and market assumptions.
- Write a baseline prompt. Include the job-relevant facts and ask for a specific output, such as a recommended opening offer and the reasoning behind it.
- Create matched variants. Change one cue at a time—such as a name or pronoun—while keeping all other wording and job details constant. If testing university or major cues, change only the relevant field. This helps isolate whether the changed cue corresponds with a change in output.
- Test cues relevant to the deployment. Include demographic signals the system may encounter, and consider intersections only when the sample and intended use support meaningful comparisons. The cited study tested gender, university and major; it did not establish results for race, disability, age, nationality or every intersectional combination.
- Repeat each prompt and preserve the setup. Record the exact prompt, timestamp, model and version label, settings, full output and run identifier. Keep results for different model versions and prompt perspectives separate. Repeated responses help show whether an apparent difference is consistent or simply one variable output.
- Choose measures before reviewing results. Compare numerical recommendations as the same kind of figure—for example, opening offer against opening offer. Also code advice strategy, confidence, caveats and references to market information. Decide in advance how you will treat missing figures or answers that do not follow the requested format.
- Report the comparison and its limits. Show the tested conditions, number of runs, differences, uncertainty and variation across runs. Note plausible alternative explanations and avoid treating a measured difference as proof of intent, cause or legal liability.
What to compare and report
A useful audit report makes the conditions visible enough for someone else to understand what the comparison means. Include the following dimensions where they apply:
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- Demographic cue: the specific name, pronoun or other signal changed; identify any intersectional combination actually tested.
- Model and date: provider, model/version label, test date and relevant settings.
- Prompt perspective: employee, employer or neutral framing.
- Scenario: job, market, location, qualifications and requested compensation measure.
- Outputs: numeric recommendations, qualitative tactics, confidence, caveats and market-information references.
- Repeatability: number of runs and how much results varied within each matched condition.
Do not collapse results across versions or perspectives if they behave differently. In the 2025 PLOS ONE experiment, those conditions were associated with substantial variation, so an overall average could conceal meaningful differences.
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How to interpret a disparity without overclaiming
A difference between matched outputs is a signal to investigate, not an explanation of its own cause. The NIST AI Risk Management Framework treats bias as broader than whether training data are demographically representative: it can be systemic, computational or statistical, and human-cognitive. A prompt-based audit can reveal an outcome pattern without identifying which mechanism produced it.
Interpret the size and consistency of a difference alongside the test design. Check whether it persists across repeated runs, whether it appears in more than one output measure, and whether it changes by model version or prompt perspective. State the limits of the cases tested; a result for one cue or occupation does not establish how untested groups or settings will be treated.
How this differs from auditing an employer’s actual pay
If the concern is what an employer pays its workforce, auditing chatbot advice is not a substitute for examining compensation practices. The US Equal Employment Opportunity Commission’s compensation discrimination guidance describes identifying similarly situated employees, comparing compensation and evaluating explanations; systemic investigations may use statistical analysis. It also recognizes that discrimination concerns can involve neutral practices with adverse impact and decisions affecting promotions, appraisals, work assignments or training.
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That is a legal investigative frame for employer compensation—not a ready-made certification test for a salary-advice chatbot. Keep the evidence from an AI output audit distinct from evidence about actual employment decisions and pay.
What employers should keep in mind
The EEOC’s 2021 announcement of its AI and Algorithmic Fairness Initiative states that existing US federal employment discrimination rules apply to AI-supported employment decisions. The announcement also quotes then-chair Charlotte A. Burrows warning that tools may “mask and perpetuate bias or create new discriminatory barriers to jobs.” The cited US materials do not supply a universal legal test for every salary-advice application or jurisdiction; legal questions depend on the system’s use and applicable law.
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