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Check an AI recruiting tool as part of your employment-selection process, not as a neutral software feature. Inventory every score, ranking, classification, recommendation, interview analysis and background-screening step; identify the model and data behind each step; test selection and error outcomes across protected and intersecting groups; verify that the use is job-related; provide accessible alternatives and accommodation routes; preserve reproducible evidence; and obtain legal review for every jurisdiction where the tool operates.
A vendor’s statement that a system is “fair” or “bias-free” is not a compliance determination. U.S. anti-discrimination and disability laws still apply when software makes or supports a hiring decision. New York City’s Local Law 144 adds specific obligations for covered automated employment decision tools (AEDTs), while recruitment and selection systems can be high-risk under the EU AI Act. The process below gives you a defensible way to evaluate a tool before launch and monitor it after deployment.
1. Define exactly what the tool decides
Start with the decision boundary. A system that only stores applications or schedules interviews presents a different risk profile from one that rejects applicants, ranks them, assigns a score or recommends whom a recruiter should interview. Document the boundary in plain language before asking whether the product is compliant.
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Inventory every automated function
| Recruiting stage | Possible automated output | Questions to record |
|---|---|---|
| Sourcing and search | Candidate matches, search rankings or suggested outreach lists | Does the ranking change who receives an opportunity? Which fields influence it? |
| Application screening | Eligibility label, score, rejection or shortlist | Is a person excluded automatically, or does a recruiter review every result? |
| Interview selection | Recommendation, prioritization or interview invitation | How much discretion remains after the recommendation? |
| Video, speech or personality analysis | Facial, voice, language or behavioral score | Could disability-related speech, movement or communication differences affect the output? |
| Assessments and tests | Timed score, proficiency classification or ranking | Are the timing, interface and tasks essential to the job? |
| Background screening | Risk flag, classification or recommendation | What data source and matching logic produce the flag, and how can it be corrected? |
| Internal mobility | Promotion, retention or succession recommendation | Are current employees evaluated by the same criteria and safeguards? |
For each output, write down the affected decision (screening, interview selection, hiring, promotion or retention), the human override, the vendor, the geography, the business unit and the model version. New York City defines an AEDT as a process that issues a score, classification or recommendation and substantially assists or replaces discretionary employment decisions. A nominal “human in the loop” does not remove the issue if the person routinely follows the system’s result.
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2. Treat fairness marketing as an evidence request
Request documentation for the exact model and version you will use, not a general product brochure. Put the requests in the procurement record and make delivery a contract condition where possible.
Minimum vendor evidence
- Model identity: model name, version, release date, configuration, thresholds and the product features enabled for your account.
- Intended use: jobs, populations, languages and decision stages for which the vendor validated the system. A model validated for one occupation cannot automatically be assumed job-related for another.
- Data provenance: descriptions of training, evaluation and reference data; collection dates; geographic coverage; labeling methods; and known gaps.
- Validation: evidence that the output relates to the skills or qualifications required for the job, with the validation design and outcome measures.
- Subgroup results: selection, error and calibration results by protected group and, where sample sizes permit, by intersections such as race and sex or age and disability.
- Proxy analysis: testing for features that can stand in for protected traits, including name, ZIP code, school, career gaps, language, voice, facial movement and device or accessibility signals.
- Limitations: populations, jobs, languages, interfaces or conditions in which performance is uncertain or known to degrade.
- Accessibility testing: screen-reader and keyboard support, captions, timing alternatives, input methods and testing with disabled users.
- Change history: release notes, model and data changes, threshold changes, retraining dates and a way to identify which version produced each decision.
- Monitoring and incidents: planned metrics, alert thresholds, complaint handling, incident history, escalation contacts and a process to pause or roll back the tool.
- Data controls: retention, deletion, access, secondary use, security and correction procedures.
- Audit cooperation: rights to inspect relevant records, obtain necessary extracts and receive timely assistance with an independent audit or regulator inquiry.
If a vendor will not disclose enough information to reproduce or challenge a result, record that limitation as a procurement risk. A confidentiality clause may protect trade secrets, but it should not prevent you from testing outcomes, investigating complaints or meeting legal obligations.
3. Test selection rates, errors and downstream results
Use representative historical or prospective samples and a job-related success criterion. Do not test only the final hiring rate: an apparently equal hiring outcome can conceal unequal rejection at the screening or interview stage.
Build a reproducible test
- Define the population. Specify the jobs, time period, locations, applicant sources and model version. Explain how protected-group information is obtained lawfully and how it is secured.
- Freeze the configuration. Record prompts, feature mappings, thresholds, tie-breaking rules, human-review instructions and the date of each run.
- Measure each stage. Calculate selection rates for screening, interview, recommendation and hiring. A selection rate is the number selected from a group divided by the number of applicants in that group.
- Measure errors. Compare false positives (people flagged as qualified who do not meet the job-related criterion) and false negatives (qualified people screened out), as well as calibration and downstream performance.
- Examine intersections. Analyze combinations such as race and sex, age and disability, or national origin and language when the data support a reliable comparison.
- Investigate proxies. Remove or isolate features that may encode protected status, and test whether the output changes materially when those signals are controlled.
- Review individual cases. Trace surprising results back to the input, model explanation, recruiter action and final outcome. Look for systematic patterns rather than treating each complaint as an isolated error.
- Preserve artifacts. Keep the test data description, code or configuration, thresholds, dates, outputs, model version and reviewer decisions so another reviewer can reproduce the analysis.
The EEOC describes adverse impact as a substantially different selection rate that disadvantages a race, sex or ethnic group. That description is not a universal pass/fail formula; the significance of a difference depends on the job, data quality, sample size, process and applicable law. Have counsel interpret the result rather than relying on an informal threshold.
Use job-related success criteria
Validate the output against requirements that genuinely matter to the job: demonstrated skills, required credentials or a documented performance measure. Avoid validating a screener only against historical manager ratings or past hiring decisions if those decisions may themselves reflect bias. Historical labels can reproduce the pattern the audit is meant to detect.
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4. Check disability and accessibility safeguards under the ADA
The EEOC and Department of Justice warn that algorithmic tools can discriminate against disabled applicants. A system may screen out a qualified person because of a disability-related trait even when the person could perform the essential job functions with reasonable accommodation. The EEOC’s May 12, 2022 technical assistance specifically identifies video-interview speech analysis as a risk: speech patterns associated with a disability can produce a low score.
Test the whole candidate experience
- Confirm that application pages, assessments and interview interfaces work with common screen readers and keyboard-only navigation.
- Provide captions, transcripts or alternative communication methods for audio and video tasks.
- Offer extra time or an untimed alternative when speed is not an essential job requirement.
- Check whether facial movement, eye contact, posture, speech rate, accent or voice quality affects a score.
- Test magnification, color contrast, text alternatives and non-pointer input.
- Ensure an applicant can request an accommodation without disclosing unnecessary medical information to the scoring system.
- Route accommodation requests to trained human staff who can provide an equivalent assessment or bypass a feature that is not essential.
Publish an accommodation path
Give applicants a prominent contact method, response time expectation and escalation route before they begin an automated assessment. Train recruiters not to treat a missing score as a rejection. Preserve the request, the alternative offered, the decision and the reason. The process must also avoid prohibited disability-related inquiries or medical examinations before they are legally allowed.
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5. Meet New York City Local Law 144 requirements
Local Law 144, enacted by the New York City Council on December 11, 2021, applies to covered AEDTs used for employment decisions in New York City. Confirm coverage with counsel based on the tool’s actual function, the employer or employment-agency role and the people affected.
Operational requirements for covered AEDTs
| Requirement | What to do | Timing or qualification |
|---|---|---|
| Independent bias audit | Obtain an audit that evaluates the tool’s impact on required demographic groups and is conducted by an independent party. | The audit must be no more than one year old before the covered use. |
| Public summary | Publish a summary of the most recent bias audit and the tool’s distribution date. | Make it public before use as required by the law. |
| Candidate notice | Provide the required notice that an AEDT will be used and explain the applicable data and retention disclosures. | Follow the law’s notice timing and content rules for the deployment. |
| Accommodation instructions | Tell candidates how to request a reasonable accommodation or an alternative process. | Make the instructions accessible and operational, not merely a generic email address. |
Keep the audit report, public summary, publication date, notices, accommodation records, configuration and model version together. A vendor’s internal fairness test is not automatically the independent bias audit required by Local Law 144. Recheck the audit age before each covered use and after a material model, data, threshold, job or workflow change.
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6. Classify EU recruiting uses under the AI Act
Under the EU AI Act, systems used for recruitment and selection that match, rank or score candidates may be classified as high-risk. That classification makes procurement and deployment controls important even when a vendor calls the product an assistant rather than a decision-maker.
Evidence to require for a potentially high-risk system
- Risk-management documentation covering foreseeable harms and mitigations.
- Technical documentation describing the system, data, intended purpose and performance limits.
- Automatic logging sufficient to reconstruct significant events and outputs.
- Transparency information that explains the system’s role to deployers and affected people.
- Human-oversight controls, including authority to disregard, reverse or stop an output.
- Post-deployment monitoring, incident handling and change management.
EU obligations and dates can depend on the system, provider, deployer and use case. Obtain current advice for the countries and hiring entities involved rather than assuming that a U.S. assessment satisfies EU requirements.
7. Map U.S. obligations beyond New York City
For deployments elsewhere in the United States, map the tool to Title VII, the Americans with Disabilities Act, age-discrimination, national-origin, religious, genetic-information, privacy and applicable state or local requirements. The EEOC states that algorithmic tools can create discriminatory barriers, and its initiative says that bias arising from algorithms and AI falls within the Commission’s priority to address systemic discrimination.
Do not assume a contract transfers responsibility to the vendor. The employer or staffing firm that uses the output remains responsible for how the selection process affects applicants and employees. Ask counsel to identify record-retention, notice, consent, privacy and automated-decision rules for each location, especially where the tool analyzes biometric, voice, video or other sensitive data.
8. Use NIST AI RMF to organize governance
NIST released AI Risk Management Framework 1.0 on January 26, 2023. It is voluntary and does not replace a statute, regulation or legal opinion, but its structure can make an internal program consistent and auditable.
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Apply the framework to recruiting
- Govern: assign an accountable owner, approve policies, train recruiters and define escalation and stop-use authority.
- Map: describe the purpose, affected people, workflow, jurisdictions, data sources, dependencies and foreseeable harms.
- Measure: run subgroup and intersectional tests, accessibility checks, validation studies and drift monitoring; record uncertainty and limitations.
- Manage: prioritize mitigations, implement accommodations, document residual risk, respond to incidents and decide whether to continue, modify or discontinue the tool.
Re-test after a model or data release, threshold change, new job family, changed applicant population, vendor change or workflow modification. Set stop-use criteria in advance, such as an unexplained subgroup disparity, a reproducible accessibility failure, inability to provide an accommodation or loss of auditability.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.9. Compare tools on evidence, control and recourse
When choosing among products, compare the controls below rather than relying on a single fairness score.
| Comparison axis | Evidence to request | Warning sign |
|---|---|---|
| Explainability | Clear description of influential features and usable explanations for individual results | Only a marketing label or an opaque score |
| Subgroup performance | Selection, error and calibration results, including intersections where feasible | One aggregate accuracy number |
| Disability support | Accessibility test results, alternatives, accommodation workflow and trained support | No way to bypass a problematic modality |
| Job-related validation | Validation design tied to the essential requirements of each job family | Validation against “top performer” history without examining bias |
| Auditability | Version IDs, logs, configuration snapshots and independent-audit cooperation | Results cannot be reproduced after an update |
| Human control | Override, appeal, correction and stop-use procedures | Recruiters are measured on following the recommendation |
| Data governance | Provenance, retention, deletion, access and secondary-use limits | Unclear data sources or indefinite retention |
| Monitoring | Drift metrics, incident response, notification and rollback plan | Testing ends at launch |
| Jurisdiction coverage | Mapping to each city, state, country and employment entity | “Compliant everywhere” without a legal analysis |
| Contract rights | Inspection, testing, data access and cooperation clauses | Terms prohibit meaningful review |
10. Keep a complete audit record
A reviewer should be able to reconstruct what happened to an applicant without relying on memory or a vendor’s current interface. Retain, subject to applicable retention and privacy rules:
- the tool inventory and decision-boundary statement;
- vendor model, version, release and change records;
- intended-use and job-related validation documents;
- data dictionaries, provenance descriptions and feature or proxy analysis;
- test datasets, scripts or configurations, thresholds and dated outputs;
- subgroup, intersectional, accessibility and accommodation findings;
- notices, public audit summaries and distribution dates where required;
- complaints, corrections, human overrides, incidents and remediation decisions; and
- the approval, legal review, monitoring results and stop-use decisions.
Limit access to sensitive demographic and accommodation information, separate it from routine recruiter views, and document the retention period and deletion method.
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11. Common failure modes and practical fixes
“The vendor says it is bias-free.”
Why it fails: the claim may not identify a model version, population, metric or job. Fix: require reproducible subgroup results, limitations, validation and change records for your configuration.
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“A human reviews the score, so the law does not apply.”
Why it fails: a recommendation that substantially influences a decision can still be an AEDT, and human reviewers can inherit automation bias. Fix: measure how often reviewers override the system, train them to challenge it and preserve the reason for each override.
“Equal hiring rates prove fairness.”
Why it fails: unequal rejection may occur earlier, and aggregate rates can hide intersectional or disability-related errors. Fix: test every stage, error type and relevant intersection against job-related criteria.
“The default assessment works for everyone.”
Why it fails: timed, audio, video or pointer-based tasks can screen out qualified disabled applicants. Fix: offer accessible alternatives, a clear accommodation channel and human review before a low score becomes a rejection.
“We audited it once.”
Why it fails: model, threshold, job and applicant-population changes can alter outcomes. Fix: monitor continuously and re-test after each material change; for NYC, verify that the independent audit is still within the one-year limit.
“The contract makes the vendor responsible.”
Why it fails: contractual indemnity does not erase the employer’s duties to applicants or regulators. Fix: retain internal ownership, audit rights, incident cooperation and a decision to pause use when evidence is inadequate.
12. A launch decision you can defend
Approve production use only when the evidence answers all of these questions:
- What exact decision does the system influence, and in which jurisdictions?
- Which model and version produced the output, using what data and features?
- Is the output validated against essential, job-related requirements?
- What are the selection, false-positive, false-negative and downstream results by protected and intersecting group?
- Have proxy features, disability effects and accessibility barriers been tested?
- Can an applicant obtain an accommodation or equivalent human process without penalty?
- Are required NYC audit, publication, notice and disclosure steps complete where applicable?
- Have potentially high-risk EU uses received the required risk, documentation, logging, transparency, oversight and monitoring controls?
- Who owns monitoring, complaints, incidents, corrections and the stop-use decision?
- Can another reviewer reproduce the result from retained records?
If any answer is “not known,” treat the tool as unready for that use. The absence of a universal pass/fail test is not permission to guess; it is a reason to narrow the use, add safeguards, seek jurisdiction-specific legal advice or defer deployment.
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