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To audit an AI hiring or workforce tool, map exactly how its output changes a decision, measure outcomes at each stage for relevant groups, test job-relatedness and disability access, document human oversight, and complete the notices, publication and retesting required in the jurisdictions where you use it. A human making the final click does not remove risk if a score, ranking or recommendation materially shapes who is interviewed, promoted, scheduled, evaluated or paid.
This guide covers applicant screening, promotion recommendations, assessments, scheduling, work allocation and performance systems. The legal rules differ by use and location: New York City Local Law 144 is narrower than the broader federal and European concerns that can apply to employment-related algorithms.
What an audit must establish
An audit is an evidence-based review of a system, its decision context and its real effects—not a vendor statement that a model is “fair.” At minimum, you should be able to answer:
- Which tool, model and version was used?
- What data entered it, and what score, class, rank or recommendation came out?
- At which decision stage did that output affect a person?
- Which applicants or workers were included, excluded or missing from the analysis?
- Are outcomes different across relevant protected or legally sensitive groups?
- Does the procedure measure skills required for this job and purpose?
- Can a qualified person with a disability use the process or request an accommodation?
- What did human reviewers actually do with the output?
- What was fixed, who approved the fix and when was the system retested?
Keep legal conclusions separate from fairness or performance findings. A disparity is a warning that requires investigation; it is not, by itself, a complete legal finding. Conversely, a result that looks balanced on one metric does not prove validity, accessibility or compliance.
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How do I audit AI hiring tools for bias and compliance?
1. Build a complete inventory and decision map
Start with every computational system that can change access to work. Include applicant ranking and sourcing, résumé or application screening, interview or assessment scoring, promotion recommendations, shift and task allocation, performance ratings, retention or pay recommendations and other tools that influence employment conditions.
For each system, record the vendor, product and model version; the intended use; the business owner; users; affected applicant or worker populations; input fields and data sources; output and thresholds; the decision stage; and the human review or override path. Note whether the tool is used only for administration or whether it can change access to a job, shift, pay or advancement. Preserve configuration, prompts, feature lists and threshold settings so a later reviewer can reproduce the scope.
Map the full path rather than just the software. For example, an assessment score may determine who reaches a recruiter, a recruiter may select an interview slate, and a manager may later use a recommendation in a promotion panel. Audit each hand-off and the cumulative result.
2. Determine which rules apply to the actual use
For New York City, compare the deployment with the city’s AEDT definition and covered employment-decision scope. The code describes a computational process derived from machine learning, statistical modelling, data analytics or artificial intelligence that produces a simplified output—such as a score, classification or recommendation—and substantially assists or replaces discretionary decision-making. The covered city decisions are screening candidates or employees for hiring or promotion in the city.
That definition is not a reason to ignore other uses. A scheduling or work-allocation system can affect hours, conditions or progression and may raise federal discrimination concerns even when it is outside Local Law 144’s hiring and promotion wording.
In the European Union, the European Commission AI Act Service Desk’s employment examples identify candidate matching, ranking, sourcing and assessment scoring as potentially high-risk. Examples also include some systems that schedule or allocate work in ways that affect conditions or advancement. The page distinguishes limited procedural tasks and certain retrospective pattern-detection audits from systems that materially influence selection. Classify the actual system under the regulation and check the obligations and dates that apply to your deployment.
3. Set the audit population and decision stages
Define the period, jobs, locations, workers or applicants and decision stages before calculating results. Preserve the number evaluated, the number advancing or selected and the denominator at every stage. Use consistently defined groups and time windows, and document how you handled missing or unknown demographic information and small groups.
Measure both the tool’s output and the end-to-end process. Earlier human screening can create a disparity before the model runs; later discretionary decisions can create or mask one after it runs. A tool can look neutral in isolation while its use in the complete process produces unequal access.
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How to measure disparate outcomes without overclaiming
Selection rates and score distributions
For each stage, calculate the proportion of each relevant group that advances, is selected, receives a high score or is assigned the opportunity at issue. Also examine score distributions, exclusion rules, missing-data rates, threshold effects and downstream outcomes when they are available. Keep the underlying counts with every rate so a reader can see whether a result rests on a large population or a very small sample.
The EEOC’s selection-procedure guidance explains that disparate-impact analysis ordinarily requires statistical analysis. It directs attention to the challenged practice, its effect on protected groups, whether the procedure is job-related and consistent with business necessity, and whether an equally effective less-discriminatory alternative exists. Do not present one ratio, cutoff or “safe” percentage as a universal answer for every jurisdiction, job or sample.
Investigate the cause, not just the number
When a disparity appears, trace it to the features, labels, threshold, workflow or human action that produced it. Check for proxy variables, historical labels that encode past decisions, exclusion of nonstandard career paths, language or accessibility effects, and differences in who reaches each stage. Compare results before and after a threshold or feature change, but record the dates and other changes that could affect the comparison.
Run sensitivity checks where sample size permits and state uncertainty and limitations. If a group is too small for a reliable estimate, report that limitation and use a privacy-protective qualitative review rather than hiding the group or treating an unstable rate as proof of equality.
What a result does—and does not—prove
A disparity metric identifies a question for legal and technical review. It does not establish intent, job-relatedness, business necessity or liability by itself. A balanced metric does not establish that the test predicts performance, that an accommodation was available or that the process complied with required notices. Keep those questions in separate findings.
Test whether the procedure is valid for the job
Connect every input and criterion to an actual qualification
For each feature, question, assessment item and scoring rule, state which skill or qualification it is intended to measure and why that skill is needed for the specific role. Check whether the administration and scoring are consistent, whether the method was validated for that job and purpose, and whether the job requirements have changed since validation.
Ask vendors for technical, validation and change-log documentation, but do not outsource the employer’s responsibility. The EEOC says vendor materials can assist an employer while the employer remains responsible for ensuring that tests are valid under the Uniform Guidelines on Employee Selection Procedures. Retain your own job analysis, validation rationale and approval record.
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Evaluate less-discriminatory alternatives
If a procedure excludes a protected group, identify another method that could predict the required performance with less adverse impact. Compare its effectiveness, administration burden, security and accessibility. Record why you retained, changed or removed the original procedure. Historical hiring success is not enough: the relevant question is whether the current method measures skills needed for this role and is justified under the applicable law.
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Revalidate after material change
Trigger a review when the model or version, training data, job requirements, score threshold, assessment format, language, user group or deployment context changes. A model validated for one job family or location should not silently be treated as validated for another.
Audit disability access and accommodation
Test abilities that are not essential to the job
Determine whether the tool depends on speech, vision, hearing, motor behaviour, facial movement, response speed, internet bandwidth or another ability that is not essential to the role. Ask whether it measures the intended skill or an impairment-related difference. Test keyboard navigation, screen-reader compatibility, captions, alternative input, timing flexibility and other relevant access paths with people who use them.
The EEOC and DOJ disability guidance warns that design or use of algorithmic tools can screen out qualified people with disabilities and can raise improper disability-related inquiry or medical-examination concerns. Limit collection to what is necessary, protect sensitive information and separate accommodation information from evaluators who do not need it.
Provide a workable accommodation route
Before deployment, publish a private contact route, response time and escalation owner for accommodation requests. Offer an alternative selection process when the tool cannot measure the essential skill without the disability-related barrier. Test the route with realistic timing; a theoretical option that cannot be arranged before a deadline is not a practical safeguard.
For a New York City covered tool, the advance notice must say the tool will be used, identify the job qualifications and characteristics it uses, and allow a candidate to request an alternative selection process or accommodation. Keep evidence that the notice and request process operated as described.
Review human oversight and real-world influence
Document what reviewers see and do
Record whether a recruiter or manager sees the score before making a decision, how much weight it carries, whether an override is technically and practically possible, which reasons are recorded and whether reviewers routinely follow the recommendation. Examine override rates and reasons across groups. A nominal human-in-the-loop control is weak if reviewers rarely depart from the output or cannot explain why they did.
The European Commission’s employment examples treat a ranking as materially influential even when recruiters can review or override it. Assess practical influence, not just the presence of a human sign-off.
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Train users on the tool’s limits, prohibited uses, accommodation handling and escalation process. Monitor whether managers use a screening score for decisions the system was not validated to support. If later hiring or performance labels are fed back into training data, review whether an earlier disparity is being reinforced.
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New York City Local Law 144: operational checklist
For a covered automated employment decision tool (AEDT) used to screen a candidate or employee for hiring or promotion in New York City, the statute requires all of the following:
- A recent bias audit: complete it no more than one year before the use.
- Public availability: publish the latest audit summary and the tool’s distribution date on the employer’s or employment agency’s website before use.
- Advance notice: provide covered New York City-resident applicants or employees the specified information at least 10 business days before use.
- Alternative or accommodation information: tell the person the tool will be used and allow a request for an alternative selection process or accommodation.
- Tool information: state the job qualifications and characteristics the tool uses.
- Written-request disclosures: make specified data-source, data-type and retention information available when it is not already posted.
The NYC Department of Consumer and Worker Protection overview says enforcement began July 5, 2023 and identifies missing audits, unpublished summaries and missing notices as complaint grounds. The hosted code warns that its database may not reflect the latest legislation or rules, so confirm the current statute, rule and agency FAQ before relying on operational details.
Keep the audit date, publication URL, distribution date, notice versions, delivery records, requests, responses and approvals with the underlying analysis. These are separate compliance controls; publishing a summary does not substitute for notice, and notice does not substitute for a valid audit.
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Federal employment-discrimination laws apply to selection procedures, including algorithmic processes. Under the EEOC’s framework, a neutral procedure that disproportionately excludes a protected group can create disparate-impact risk unless the required legal justification is met. The analysis focuses on the challenged practice, job-relatedness and business necessity, and whether an equally effective less-discriminatory alternative exists.
Disability obligations add accommodation, screening-out and medical-inquiry risks. Review the tool’s questions, inputs, interface, timing and output—not only its final ranking. The EEOC’s 2007 technical-assistance page is guidance, not law; for a live decision, verify current statutes, regulations, agency materials and controlling case law with qualified counsel.
For historical context only, that 2007 EEOC technical assistance reported 304 employment-testing and screening-related discrimination charges in fiscal year 2007. It is not a current prevalence estimate.
In a May 12, 2022 release on AI and disability discrimination, EEOC Chair Charlotte A. Burrows said, “New technologies should not become new ways to discriminate.” Assistant Attorney General Kristen Clarke said, “Algorithmic tools should not stand as a barrier for people with disabilities seeking access to jobs.” The statements underscore the agencies’ concern but do not replace the legal analysis of a particular tool.
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European Union scoping
Recruitment and selection systems that match, rank, source or score candidates may fall into the European Union AI Act’s high-risk employment examples. Some systems that schedule or allocate work can also be relevant when they affect working conditions or progression. The Commission’s examples distinguish a material decision influence from limited procedural tasks and certain retrospective audits that do not themselves make decisions.
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Use those examples to identify systems for a detailed classification, then check the regulation’s applicable obligations, responsible parties and dates for your deployment. Do not assume that a tool’s label, marketing category or a human override determines its legal classification.
What to keep in the audit file
A defensible record should let an independent reviewer reproduce the scope and understand what happened:
- System name, supplier, model and version, configuration and deployment dates.
- Purpose, jobs, locations, decision stages, users and affected populations.
- Input fields, data sources, labels, retention periods and data-quality checks.
- Output definition, thresholds, human review, override controls and training.
- Population counts, denominators, group definitions, missing-data treatment and statistical methods.
- Selection rates, score distributions, exclusions, uncertainty and downstream outcomes.
- Job analysis, validation evidence and the assessment of less-discriminatory alternatives.
- Accessibility tests, accommodation route, requests, outcomes and privacy controls.
- Reviewer independence, conflicts, findings, limitations and severity decisions.
- Remediation owner, deadline, implementation evidence and retest results.
- Required notices, publication records, distribution dates and approval decisions.
The sources do not prescribe one universal report template. Design the record so another qualified reviewer can understand what was tested, what was not tested and why the deployment was approved, changed, paused or retired.
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Remediate and retest
Correct data or threshold problems, remove features that are not job-related, improve accessibility, change the workflow or add a validated alternative. Assign an owner and deadline, then rerun the relevant checks after the change. Do not close a finding merely because a future fix is planned.
Pause use
Pause a deployment when you cannot explain a material disparity, cannot provide a required accommodation or notice, lack evidence that the procedure is valid for the job, or discover that the tool is being used outside its approved purpose. Preserve records and communicate the interim process to affected decision-makers.
Retire or replace
Retire the tool when no equally effective remediation is available, the use case has changed beyond the validation evidence, required data cannot be collected lawfully or safely, or an alternative process meets the business need with lower risk.
How to compare an audit provider or method
If you hire an independent employment AI auditor, compare the engagement—not just a logo or a “bias score.” Ask for evidence on:
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| Independence | Who pays, who performed model development, and what conflicts or incentives exist? |
| Legal scope | Does the work address the jurisdictions and uses in your deployment, while distinguishing legal compliance from broader fairness or performance evaluation? |
| Statistics | Which outcomes and denominators are used, how are small samples and uncertainty handled, and can calculations be reproduced? |
| Job validity | Does the team perform job analysis and selection-procedure validation for the specific role? |
| Accessibility | Are disability access, accommodation and possible medical-inquiry issues tested? |
| Data governance | How are provenance, privacy, retention, security and sensitive demographic data controlled? |
| Version coverage | Will the review cover the model, configuration, thresholds and versions actually deployed? |
| Transparency | Will methods, limitations, code or calculations and evidence be documented sufficiently for review? |
| Remediation | Does the engagement include practical fixes, an owner-ready action plan and retesting? |
Independence and qualifications must be verified from the provider’s methodology and conflicts disclosures. No provider is automatically qualified because it offers a “bias audit.”
Common audit failures and fixes
- Auditing only the model: Map the entire decision path, including human stages before and after the output.
- Using one universal threshold: Treat metrics as evidence for investigation, then apply the legal and job-validity analysis for the actual context.
- Relying on vendor validation: Obtain the materials, but retain the employer’s own job analysis, validation decision and change control.
- Ignoring small groups or missing data: Report denominators and uncertainty, investigate data quality and use privacy-protective alternatives where estimates are unstable.
- Offering accommodation only in theory: Test the request route, response time and alternative process before launch.
- Assuming a human override cures bias: Measure how often overrides occur, why they occur and whether reviewers actually use them.
- Publishing a summary and stopping: Track notices, distribution dates, remediation, retesting and material changes as an ongoing governance cycle.
- Using an old audit after a change: Reassess when data, model version, threshold, job requirements or deployment context changes.
A practical release gate
Before approving a new or changed tool, require written answers to these questions:
- Is the use case and jurisdictional scope clearly defined?
- Do we know the exact model, data, version, threshold and decision stage?
- Are group definitions, denominators, missing data and limitations documented?
- Have we reviewed both tool outputs and end-to-end outcomes?
- Is each criterion job-related, validated and administered consistently?
- Was a less-discriminatory alternative considered?
- Can people with disabilities use the process or obtain a timely alternative?
- Are human review, overrides and reasons recorded and monitored?
- Are required NYC publication and notice controls complete, where applicable?
- Is there an owner, deadline and retest plan for every finding?
If any answer is “no,” the decision should be to remediate, pause or obtain a documented exception from the responsible legal and governance authority—not to treat the missing evidence as proof that the system is safe.
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