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To audit an AI salary tool, give it matched prompts that differ only in a demographic cue, repeat each test, and compare both the numbers it recommends and the negotiation advice it gives. Keep the model version, prompt perspective, job context, and test date fixed and documented. This tests the tool you actually prompted—not an employer’s full pay system or what workers ultimately earn.
First decide what system and outcome you are auditing
“AI agent pay” can mean several different things. An AI may advise a worker on a requested salary, advise an employer on an offer, or act on one side’s behalf in a negotiation. Those are distinct uses; an audit result for one does not automatically describe the others.
Define the decision you want to test before writing prompts. It might be an opening salary, a target, a counteroffer, a negotiation tactic, or a final compensation package. Record the intended user, job, location, market, and how the AI is used. A useful audit question is specific: does changing a gendered name or pronoun change the salary recommendation for an otherwise identical candidate and role?
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Set a baseline case
Write one prompt with the job, location, experience, qualifications, and requested output stated clearly. Make the role and relevant facts realistic for the market you are testing.
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Create matched variants
Change one cue at a time—such as a name or pronoun—while keeping all job-relevant information and the requested response the same. If testing university or major as well, vary those separately rather than changing several details in one prompt. This makes it easier to identify which change tracks with a difference in output.
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Test the perspectives that match deployment
If the tool will advise candidates, phrase the prompt from the employee’s perspective. If it will advise an employer, test employer-voiced prompts too. A neutral formulation can be a useful additional condition, but it should not replace the actual use case.
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Repeat the prompts and preserve the setup
Run each matched case multiple times. Save the exact prompt and response, timestamp, model and version label, settings, and a run identifier. A single response cannot show whether a difference is stable or just one variation in generated output.
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Choose measures before reviewing outputs
Compare recommended opening offers or targets numerically, using the same outcome for every case. Also inspect whether the advice changes in confidence, negotiation strategy, caveats, or use of market information. Decide in advance how you will code qualitative differences so the interpretation is not shaped after seeing the results.
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Report the conditions and uncertainty
Show the cue tested, any intersectional combination, the model and test date, prompt perspective, job-market scenario, number of repeated runs, and the spread of results. Keep results for different model versions and perspectives separate when they produce materially different outputs.
What existing evidence says about gender and salary recommendations
A 2025 peer-reviewed PLOS ONE study examined salary negotiation advice in a simulated US technology job market. The researchers submitted 98,800 prompts to each of four ChatGPT versions, varying the employee’s gender, university, and major, and whether the prompt was voiced as the employee or the employer.
The study reports statistically significant gender-associated differences in recommended offers for all four tested models, though those gaps were smaller than differences associated with some other tested attributes. Differences between model versions and between employee- and employer-voiced prompts were larger. This is evidence about the study’s prompts, scenario, and versions—not all AI systems, occupations, or real-world pay outcomes. The authors also caution against treating their contextual test as a general certification that models are biased or unbiased.
The study identified gender, university, and major as the cues it tested. It does not establish how the models respond to race, disability, age, nationality, or every intersectional combination. An audit should test the cues and combinations relevant to its own deployment rather than extrapolating beyond its sample.
How to interpret a difference without overclaiming
A consistent gap across matched prompts is a signal to investigate. It does not, by itself, prove discriminatory intent, establish legal liability, or identify the mechanism causing the gap. Prompt wording, model version, output variability, and the context represented in the scenario can all matter.
NIST’s AI Risk Management Framework treats bias as broader than whether training data are demographically representative. It identifies systemic, computational and statistical, and human-cognitive sources of bias. A prompt-based audit can reveal a difference in outputs, but cannot alone determine which source produced it.
Describe the size and uncertainty of observed differences, how much outputs varied across repeats, and the limits of the test. If results change substantially by model version or prompt perspective, report that variation rather than combining unlike conditions into a single average.
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Testing a chatbot’s salary advice answers a different question from whether an employer pays similarly situated workers differently. For an actual compensation investigation, the EEOC’s compensation guidance describes identifying comparators, examining explanations for pay differences, and using statistical analysis where appropriate. It also recognizes that discrimination concerns can involve neutral practices with adverse impact, as well as promotions, appraisals, work assignments, or training that affect compensation.
Best Value
That is a legal investigative framework for employer compensation practices, not a plug-in test or certification method for salary-advice chatbots. Keep findings from an AI output audit distinct from conclusions about an employer’s payroll or legal compliance.
US legal context
The EEOC says existing US federal employment discrimination laws apply to employment decisions supported by AI. Its 2021 announcement of the AI and Algorithmic Fairness Initiative warned that such tools may “mask and perpetuate bias or create new discriminatory barriers to jobs.” The agency’s materials are US federal sources; they do not supply a universal legal test for every salary-advice product or jurisdiction.
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