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An AI recruiting tool is compliant only when its real-world use, data, outputs, accessibility, human oversight and jurisdictional controls have been checked. Start by mapping every score, ranking, recommendation and rejection to the employment decision it influences. Then obtain model-specific evidence from the vendor, test selection and error outcomes across protected and intersecting groups, provide disability accommodations, complete the legal steps for each location and continuously monitor the system after launch.
What you are actually auditing
Do not audit an AI product by its marketing label. Audit the complete selection process in which it operates. A resume parser, search assistant or interview scheduler may have little effect on a decision; a system that filters applicants, ranks them, scores interviews or recommends rejection can materially shape who is hired.
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List every automated or partly automated function, including:
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- Screening questions, assessments and test scoring
- Interview invitations, ranking and shortlist generation
- Video, speech, facial-movement or behavioral analysis
- Recommendations to reject, advance, hire, promote or retain
- Background-screening classifications and risk scores
- Any human decision that relies on an automated score or recommendation
For each function, record the job, decision, location, vendor, product name, model version, threshold, human reviewer and affected applicants. Note whether a reviewer can override the output and whether an override is genuinely available in practice. A score, classification or recommendation that substantially assists or replaces discretionary employment decisions falls within New York City’s definition of an automated employment decision tool (AEDT).
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Write the decision boundary
Describe the boundary in one sentence, such as: “Version 4.2 ranks customer-support applicants for interview invitations in New York City, using resume text and an online assessment; recruiters may override the ranking.” If the tool also analyzes a recorded interview or sends an automatic rejection, document those as separate decision points. This prevents a vendor from characterizing a high-impact system as “just administrative software.”
What to demand from the vendor
A fairness badge or statement that a model is “bias-free” is not a compliance determination. Request evidence for the exact model, version and configuration you will use. Put the requests in procurement and contract documents so the vendor must cooperate with later audits.
Model and data documentation
- Exact model, release date, version history and a change log
- Intended use, prohibited use and the jobs or populations represented in validation
- Description of training, evaluation and reference data, including provenance and labeling methods
- Features used directly and features that may act as proxies, such as name, ZIP code, school, career gaps, language, voice, facial movement or device and accessibility signals
- Input, output and retention rules, including deletion schedules and customer access to logs
Validation and subgroup evidence
- Job-related validation showing why the output predicts a legitimate qualification or success criterion
- Selection rates, false-positive rates, false-negative rates and calibration for relevant protected groups
- Intersectional results, such as race and sex together, rather than only one demographic dimension at a time
- Confidence intervals or other uncertainty information and a description of sample limitations
- Known failure modes, excluded populations, language limitations and conditions under which the score should not be used
- Accessibility testing for screen readers, keyboard navigation, captions, speech differences, timed tasks and alternative input methods
Operational safeguards
- Monitoring plan, alert thresholds and the person responsible for responding to a disparity
- Incident history, complaints and previous corrective actions
- Human-review, appeal and accommodation procedures
- Audit cooperation, data access, confidentiality and independent-review rights in the contract
- Notification of model, data, threshold or workflow changes before they affect applicants
If a vendor cannot identify the version, data basis, validation population or limitations, you cannot establish that the evidence applies to your deployment. Treat the missing evidence as a deployment risk, not as proof that the system is neutral.
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How to test for discriminatory outcomes
Use representative historical or prospective samples and a job-related outcome that is defined before testing. Do not select a convenient sample that excludes applicants who dropped out, requested an accommodation or were rejected early. Preserve the test data, code or configuration, thresholds, dates and model version so another reviewer can reproduce the analysis.
Measure selection rates
For each protected and intersectional group, calculate the share of applicants who pass the tool’s threshold or receive the recommendation being tested. Compare the rates with the relevant comparison group and investigate materially different results. The EEOC describes adverse impact as a substantially different selection rate that disadvantages a race, sex or ethnic group; a numerical disparity is a signal for investigation, not a complete legal conclusion.
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Measure errors, not only pass rates
| Measure | What it reveals | How to use it |
|---|---|---|
| Selection rate | Who advances, is rejected or receives a recommendation | Compare protected and intersectional groups at every threshold |
| False-positive rate | How often the tool advances people who do not meet the defined job-related criterion | Check whether one group is over-advanced or exposed to later rejection |
| False-negative rate | How often the tool rejects people who meet the defined criterion | Prioritize review of groups that may be screened out despite qualification |
| Calibration | Whether the same score means a comparable likelihood of the outcome across groups | Check score interpretation before allowing recruiters to rely on rankings |
| Downstream outcome | Whether an automated recommendation is associated with later job-related results | Monitor interviews, offers, retention or performance without treating correlation as proof of causation |
Test proxies and workflow effects
Remove or isolate features that can encode protected characteristics indirectly. A ZIP code can reflect race or national origin; a school, name or career gap can correlate with sex, age or socioeconomic status; speech and facial signals can reflect disability, language background or culture. Test the effect of each feature where feasible, then test the complete workflow because a seemingly neutral score can become discriminatory when combined with a recruiter rule, a cutoff or an automatic rejection.
Test realistic scenarios
- Equivalent resumes that vary only in names, pronouns, schools, ZIP codes or employment gaps
- Applicants using screen readers, keyboard navigation, captions, interpreters or alternative input
- Different accents, languages and speech patterns where the job does not require a particular speech style
- Applicants with non-linear careers, caregiving gaps, military experience or credentials from different education systems
- Threshold changes and ties, including what happens when a recruiter overrides the score
Run the analysis before launch and again after any change to the model, training data, job description, threshold, vendor, assessment or human workflow.
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U.S. anti-discrimination and disability laws apply when software makes or supports a hiring decision. The EEOC and Department of Justice have warned that algorithmic tools can create discriminatory barriers and must not screen out a person who could perform the job with reasonable accommodation. The EEOC gives video-interview speech analysis as an example: different speech patterns associated with disability can produce a low score even when speech is not a job requirement.
Inspect the candidate experience
- Can a screen reader identify every instruction, control, error and result?
- Can a candidate complete the process using a keyboard without a time-sensitive pointer action?
- Are captions, transcripts or an equivalent alternative available for audio and video content?
- Do timed assessments allow an approved extension without changing the score logic?
- Does the system penalize atypical facial movement, eye contact, speech rate, pauses or assistive technology?
- Are mobile-device, browser and bandwidth assumptions excluding applicants with disabilities?
Provide a real accommodation path
Publish a clear channel for requesting an accommodation before the assessment or interview. State who receives the request, how quickly it is handled and what alternative formats are available. Route requests to a trained human rather than an automated mailbox that cannot pause a deadline. Keep the accommodation decision separate from the hiring score, restrict access to disability information and provide human review when an automated result may have screened out a qualified person.
Do not use the tool to solicit disability information or conduct a medical examination unless a lawful, job-related exception applies and has been reviewed by counsel. A vendor’s general accessibility conformance statement does not establish that your particular assessment, configuration and hiring workflow are accessible.
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Map the deployment to applicable law
Legal obligations depend on what the tool does, where it is used, which applicants are affected and how much discretion remains with people. A vendor contract does not transfer the employer’s responsibility for a discriminatory outcome.
| Regime | Systems or decisions covered | Key checks | Timing or trigger |
|---|---|---|---|
| U.S. federal employment law | AI tools that make or support hiring and other employment decisions | Assess Title VII and related anti-discrimination duties; prevent disability screening-out under the ADA; review age, national-origin, religious and genetic-information risks; provide reasonable accommodation | Applies whenever the tool is used in an employment process |
| New York City Local Law 144 | Covered AEDTs that issue a score, classification or recommendation substantially assisting or replacing discretionary employment decisions | Independent bias audit; public summary and distribution date; notices; accommodation instructions; required data and retention disclosures | Audit must be no more than one year before use; required public information must be available before use |
| EU AI Act | Recruitment and selection systems that match, rank or score candidates | Plan for high-risk obligations covering risk management, documentation, logging, transparency, human oversight and monitoring | Apply the requirements and dates in force for the deployment; obtain current EU-specific legal advice |
New York City Local Law 144 checklist
- Confirm that the system’s output substantially assists or replaces discretionary employment decisions and identify every New York City use.
- Obtain an independent bias audit covering the deployed tool and relevant selection outcomes. Verify that the audit date is no more than one year before the planned use.
- Publish the audit summary and the distribution date before using the AEDT. Keep the public information available as required for the deployment.
- Provide the required candidate notice, instructions for requesting an accommodation and the required data and retention disclosures.
- Preserve the audit, underlying test materials, notices, distribution date, model version, thresholds, complaints and remediation decisions.
- Stop or pause use if the audit is outdated, the tool changes materially or monitoring shows a disparity that has not been investigated.
Local Law 144 was enacted on December 11, 2021. The one-year audit limit and notice requirements are stated in the city’s law and related official guidance; verify current implementation details before each New York City deployment.
EU recruitment deployments
Under the EU AI Act, recruitment and selection systems that match, rank or score candidates may be classified as high-risk. Procurement should therefore require a risk-management process, technical and operational documentation, logging, transparency to affected people, meaningful human oversight and post-deployment monitoring. Classification and effective dates can depend on the system and the law’s implementation schedule, so obtain current advice for each EU country and use case rather than relying on a generic vendor label.
Build governance that survives model changes
NIST AI Risk Management Framework 1.0, released January 26, 2023, is a voluntary structure for organizing governance, risk identification, measurement and management. It does not replace binding employment, privacy or accessibility law. Use it as an operating system for the controls that your legal review requires.
Assign ownership
- Business owner: defines the job-related purpose and approves the decision boundary
- Legal and compliance owner: maps jurisdictions, notices, retention and accommodation duties
- Data and model owner: controls versions, features, thresholds and validation
- Accessibility owner: tests the candidate experience and accommodation path
- Independent reviewer: challenges assumptions and examines subgroup and intersectional results
- Incident owner: receives complaints, triggers investigation and can stop use
Set monitoring and stop-use rules
Track selection rates, false negatives, false positives, calibration and downstream outcomes by group at a frequency appropriate to applicant volume and risk. Define a written trigger for pausing the tool, such as a material disparity, unexplained drift, inaccessible interface, missing audit, vendor change without validation or repeated accommodation failures. Record the investigation, mitigation, decision-maker and date. Re-test after any model, data, job, threshold, vendor or workflow change.
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Preserve an evidence package
Keep the decision-boundary statement, vendor documents, validation plan, datasets or data descriptions, code or configuration, thresholds, model versions, audit reports, public notices, accommodation requests, complaints, monitoring results and remediation decisions. Record who approved production use and why the evidence was sufficient at that time. Retention must follow applicable law and the organization’s privacy policy; do not retain sensitive applicant data simply because it is convenient.
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Use the same questions for every product and compare evidence, not marketing claims.
| Comparison axis | Questions to ask |
|---|---|
| Explainability and evidence | Can the vendor explain the output and provide reproducible, version-specific validation? |
| Subgroup performance | Are protected and intersectional selection, error and calibration results available for your jobs? |
| Disability and accessibility | Were screen readers, keyboard use, captions, speech differences, timed tests and alternatives tested? |
| Job-related validation | What legitimate qualification or outcome does the score measure, and how was that relationship validated? |
| Auditability and version control | Can you retrieve inputs, outputs, thresholds, logs, release dates and change history? |
| Human override and appeal | Can a trained reviewer reverse an output, and can an applicant obtain meaningful review? |
| Data provenance, privacy and retention | Where did the data come from, how long is it kept and who can access it? |
| Monitoring and incidents | What drift, disparity and accessibility signals are monitored, and how quickly are customers notified? |
| Jurisdiction coverage | Does the evidence address each location, notice rule, audit requirement and effective date? |
| Contract rights | Can your organization inspect, test and audit the system, and require cooperation after an incident? |
Common failure modes and corrective actions
“The vendor says it is bias-free”
Replace the claim with a request for model-specific evidence, limitations and reproducible subgroup results. If the vendor will not provide them, restrict the use to a lower-impact function or do not deploy it.
“A human is in the loop, so the law does not apply”
Measure how often reviewers override the output and whether they can see the same candidates. A nominal override that is rarely used or discouraged does not erase the tool’s influence.
“The overall pass rate looks equal”
Break results down by protected and intersecting groups, error type, threshold and stage. Equal overall rates can hide high false-negative rates for a smaller group or a disparity introduced at a later workflow step.
“Accessibility is the applicant’s responsibility”
Provide an accessible alternative and a clear accommodation process before the deadline. Test the complete candidate journey, not only the vendor’s interface demo.
“The model has not changed”
Re-test when the job description, training data, threshold, recruiter workflow, integration or candidate population changes. A stable model can produce different outcomes in a new context.
“One audit covers every use”
Match the audit to the actual model version, job family, location, threshold and decision. New York City’s one-year limit is a maximum audit age for covered AEDT use, not a permanent certification.
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Approve production use only when all of the following are documented:
- The decision boundary and jurisdictions are clear.
- The exact model, version, data basis and intended use are known.
- Job-related validation and subgroup and intersectional testing are complete.
- Proxy-feature and workflow effects have been investigated.
- Accessibility testing and a staffed accommodation path are in place.
- Required notices, audits and disclosures are complete for each location.
- Human review, appeal, incident response and stop-use authority are assigned.
- Monitoring, change control and evidence retention are operational.
Do not treat a missing item as a minor procurement inconvenience. It means the organization cannot explain, reproduce or correct a decision that may affect a person’s employment opportunity. The EEOC summarized the policy risk plainly: “New technologies should not become new ways to discriminate.” It also states that bias arising from algorithms and AI falls within its priority to address systemic discrimination.
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