Audit an AI-generated skills profile claim by claim, then test how its errors and downstream effects vary across relevant groups. Start by documenting what the profile will influence; compare each stated skill with traceable evidence and important work behaviors; check for subgroup disparities, proxy signals and accessibility barriers; and assign someone to monitor, correct and revalidate the system. A profile used only for exploration has different stakes from one used to rank applicants or affect promotion.
What an audit needs to establish
A useful audit answers two separate questions: does the profile describe a person’s skills accurately, and does the system or its use disadvantage particular groups? Strong overall accuracy does not establish fair subgroup performance. Nor does a representative test set, by itself, prove fairness.
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Keep the unit of review small enough to inspect: each skill claim, its supporting evidence, and the reasoning that connects the two. Then examine the profile’s effects in the workflow where people will actually use it. A claim can be factually plausible yet irrelevant to the role, or accurate on average while producing unequal error patterns or outcomes.
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1. Define the system and its use
Before reviewing outputs, record what produced them and what people may do with them. This boundary determines what evidence you need and how serious an error could be.
- System identity: owner, vendor, model and version if known, input data, output format, and the date of the version under review.
- Intended population: the roles, applicants or employees the system covers, and any groups or contexts outside its intended scope.
- Workflow: whether it extracts skills, suggests matches, recommends people, ranks candidates, or influences hiring, promotion, training or another decision.
- Human role: who sees the profile, what they are expected to do with it, and whether they can disregard or correct it.
Keep a dated copy of representative profiles and the decision workflow. That record makes it possible to distinguish findings about one version or use from findings about another. NIST’s voluntary AI Risk Management Framework can help organize risk identification, evaluation and management; it is a framework, not a certification or substitute for legal duties.
2. Define each skill in terms of the job
For each role, use a documented job analysis or role specification to say what a skill means, how it appears in work, and whether it is needed for an important work behavior. “Leadership,” for example, is too broad to audit consistently unless the organization defines which observable actions count and why they matter to the role.
Check whether the profile treats evidence of a skill as the skill itself. A title, school, career path, writing style or employment history may correlate with an ability without demonstrating it. Ask reviewers to identify the actual evidence and explain how it supports the defined skill. EEOC guidance on content validity emphasizes an operationally defined skill or ability that is a prerequisite for critical or important work behavior, rather than a large inferential leap from indirect signals.
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3. Test accuracy at the claim level
Draw a sample from the roles and population in which the profile will be used. Have trained reviewers compare every sampled skill claim with the source material. Preserve the original evidence, the profile wording, any confidence or uncertainty shown by the system, and the reviewer’s rationale. If the tool does not expose confidence or evidence links, record that limitation rather than treating an unqualified output as certain.
For each claim, classify what reviewers find. Useful categories include unsupported claim, omitted skill, incorrect level, stale claim, ambiguous wording, and evidence that does not actually support the stated skill. Also record disagreement between reviewers and the rule used to resolve it. A clean adjudication process matters: otherwise, apparent system error can be confused with inconsistent human judgment.
For instance, if a profile assigns “advanced data analysis” based only on a job title, reviewers should check whether the source contains work products or described tasks that meet the organization’s definition of advanced analysis. If it does not, the claim is unsupported under that definition even if the title is often associated with the skill.
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Do not treat a convenient sample size or pass rate as an official standard. Set sample coverage and acceptance criteria for the roles, consequences and level of uncertainty involved, and document why those choices are adequate.
4. Examine subgroup errors, outcomes and proxies
Compare claim-level errors and downstream outcomes across relevant groups where collection and analysis are lawful and appropriate. Consider intersections when the available sample supports meaningful interpretation; small or uneven samples can make a comparison unstable, so report that limitation rather than implying certainty.
Look for more than explicit protected attributes in the input schema. Historical labels and proxy signals can reproduce discrimination even when a model never receives a protected characteristic directly. Examine features such as career path, institution, job titles or patterns in language for their role in generating claims, and investigate whether error rates or decisions differ across groups.
Representation in the evaluation data is important, but it is not proof of fairness. Agree before testing which measures matter, what variance warrants investigation, who escalates a concern, and what findings would pause use. ICO guidance calls for proactive attention to possible inferences and ongoing monitoring; it also cautions that representative data alone is insufficient. In the UK, using special-category data to assess discrimination may require both a UK GDPR Article 6 lawful basis and an Article 9 condition, with the relevant condition depending on the circumstances.
5. Check accessibility and accommodation
Review whether the profile depends on assessment formats, interaction patterns or source data that could disadvantage people with disabilities. In U.S. employment contexts, EEOC and DOJ materials warn that a tool can screen out a person who could perform the job with or without reasonable accommodation. Establish a clear way for people to request accommodation and for the organization to assess that request without treating the tool’s result as conclusive.
6. Match the evidence to the decision
The more a profile affects an employment decision, the stronger the validation and documentation should be. An internal exploration aid calls for appropriate controls; a profile used to rank applicants or influence promotion demands scrutiny proportionate to those consequences. If an employment selection procedure has adverse impact in the United States, the Uniform Guidelines framework calls for evidence of validity. Whether the profile is a selection procedure, what data can be used for analysis, and which duties apply depend on the actual use and jurisdiction; get jurisdiction-specific advice for deployment decisions.
The EEOC’s Uniform Guidelines Q&A describes the four-fifths rule as a U.S. rule of thumb: agencies will generally regard a selection rate for a race, sex or ethnic group that is less than 80% of the rate for the group with the highest selection rate as substantially different. It is not a universal threshold for model fairness, a complete impact analysis, or a definitive conclusion about legality.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.7. Choose an audit arrangement that can answer the questions
Internal review, vendor validation and independent auditing can all contribute, but they are not interchangeable. Compare them by who controls the work, what evidence is inspected, whether methods can be reproduced, and whether findings lead to monitoring and correction. These are practical comparison criteria, not a certified procurement standard.
| Approach | Potential contribution | Questions and limitations to resolve |
|---|---|---|
| Internal review | Can connect profile claims to local job definitions, decision processes and correction routes. | Check whether reviewers are sufficiently independent of the tool owner and decision-makers, and whether they can access the evidence and data needed for subgroup analysis. The guidance does not prescribe a universal level of independence. |
| Vendor-provided validation | May provide system documentation and performance evidence for the product or model version. | Ask whether evidence covers your roles, population, workflow and version; whether claim-level accuracy, subgroup effects and accessibility were tested; and whether methods and limitations are transparent enough to reproduce. A vendor test alone does not establish performance in your setting. |
| Independent third-party audit | Can add distance from the vendor and decision owner while examining accuracy, fairness and governance. | Verify access to relevant inputs and outputs, methods, expertise, reproducibility, coverage of accommodation and post-launch monitoring, and a route to remediate problems. Independence alone does not guarantee a complete or useful audit. |
8. Assign ownership, monitor and revalidate
Before launch, name the person responsible for final validation and the people responsible for fixing problems. Set a review schedule and event-based triggers, such as a model or data change, revised job definitions, or a change in how managers use the profile. ICO guidance emphasizes robust testing, continuing performance monitoring and clear responsibility for final validation before deployment and, where appropriate, after updates.
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- Provide a way for affected people and reviewers to report inaccurate or harmful claims.
- Specify how profiles will be corrected and how decisions affected by errors will be reconsidered.
- Investigate variance against the agreed limits; pause or restrict use when the evidence is inadequate for the decision at hand.
NIST’s March 2022 publication, Towards a Standard for Identifying and Managing Bias in Artificial Intelligence (SP 1270), is another reference for organizing bias-management work. Neither a framework nor a single metric can establish that a particular profile is fair or lawful.
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