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To evaluate AI recruiting software for your ATS, judge it as part of a hiring decision—not as a standalone demo. Define the task and the decision it can affect, demand evidence that it works for your roles and applicant populations, test its data flows and failure handling, and establish human oversight and monitoring. Then check the legal requirements that apply to the tool’s function and the locations where candidates or employees are assessed.
1. Define what the AI feature does—and what it can decide
“AI recruiting software” can describe very different functions: sourcing candidates, parsing resumes, ranking applicants, assessing interviews, drafting communications, or summarizing information for recruiters. The evaluation depends on which task the feature performs and how much it can affect who advances.
Document the intended use before comparing vendors. Include the roles and job families, candidate and employee populations, languages, locations, users, and point in the hiring process. State whether the feature only reduces administrative work or scores, ranks, recommends, suppresses, or rejects candidates. That distinction affects the evidence you need and may affect legal obligations. In New York City, for example, Local Law 144’s applicability turns on a tool’s function and impact, not simply whether a vendor calls it AI.
Questions for the vendor
- What is the feature’s intended purpose, and which uses are unsupported?
- What data does it use, including inferred or derived traits?
- What does it return, and how should a recruiter interpret that output?
- Can it change a candidate’s ranking or progression, or cause a person to be screened out?
- What changes to the model, data sources, or feature require retesting, and how will you be notified?
2. Ask for evidence that matches your jobs and applicants
A general accuracy claim, polished dashboard, or successful demo does not show that a feature is suitable for your hiring task. Ask for a validation package that explains the target construct, the criterion used to assess it, the sample and job context, the evaluation design, metrics and uncertainty, subgroup analyses, known confounds, and limits on generalizing the results.
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If a vendor says a system predicts “quality,” “fit,” or “potential,” ask for an operational definition of that term and evidence that the measured proxy represents the job-related concept it claims to measure. The NIST AI Risk Management Framework’s Playbook recommends documenting construct, internal, and external validity, reliability, robustness, assumptions, and operational limits. NIST also warns that proxy measures can reflect confounding or spurious associations. AI RMF is a voluntary framework, not a substitute for legal review; NIST says version 1.0 is being revised.
Test it in a controlled pilot
Use a representative, human-reviewed sample and compare the AI-supported workflow with your current process. Examine false negatives—qualified applicants the feature misses—as well as false positives, recruiter override rates, and downstream outcomes where lawful and appropriate. Break down results by role, location, language, and relevant groups only with suitable privacy and governance controls.
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These checks can reveal where a tool behaves differently, but no single metric or threshold proves that a hiring system is fair. Ask what the vendor’s test results mean for your specific use, what remains uncertain, and what evidence would cause you to pause or change deployment.
3. Test the ATS integration as a workflow, not just a connector
A successful connection does not establish that candidate data, permissions, decisions, and exceptions behave correctly in practice. Run the feature in a representative sandbox or controlled pilot using realistic cases, including duplicates, incomplete records, high-volume periods, and error conditions.
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Integration and operations checklist
- Data flow: Identify every field sent to and returned from the feature. Verify field mapping, data minimization, and which system is the source of truth.
- Identity and access: Test candidate matching, duplicate handling, recruiter permissions, and who can view or change scores and recommendations.
- Failure behavior: Test latency, failed requests, retries, and outages. Confirm the workflow can safely fall back to the existing ATS process rather than silently dropping or disadvantaging candidates.
- Records and auditability: Determine whether the system logs inputs, outputs, prompts where relevant, recruiter reviews and overrides, and model or version changes.
- Data lifecycle: Confirm retention, deletion, export, subprocessors, and whether customer data may be used to train or improve models.
- Change management: Agree how the vendor will notify you about model updates, changed data sources, or feature retirement, and what changes require renewed testing.
- Support and incidents: Establish response paths for incidents, disputed outputs, and technical failures, including who on your team owns each decision.
NIST’s Playbook suggests unit, integration, functional, and other software tests; it also recommends defining operating limits, monitoring performance, and deciding what to do when a system operates outside its validated range. The sources cited here do not establish the quality or behavior of any particular vendor’s ATS integration, so verify the integration you intend to use.
4. Verify accessibility and disability safeguards
Resume ranking, timed assessments, video interviews, and other selection steps can screen out people with disabilities. Ask the vendor to identify potential barriers in the candidate experience, demonstrate accessible steps, explain how accommodation requests are routed, and provide an alternative assessment path. Confirm that recruiters know how to pause an automated workflow and refer a request to the appropriate team.
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The EEOC and Department of Justice’s May 12, 2022 guidance highlights three concerns: providing a process for reasonable accommodations; avoiding screening out people who could perform a job with accommodation; and avoiding tools that elicit disability or medical information. EEOC Chair Charlotte A. Burrows said, “New technologies should not become new ways to discriminate.” These safeguards should be part of the workflow you test, not merely a vendor’s policy statement.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.5. Check legal requirements for each use and location
For a US deployment, assess applicable federal, state, and local employment, disability, privacy, and automated-decision requirements based on what the feature does and where candidates or employees are located. The requirements can differ by jurisdiction and use; the sources discussed here are not a complete fifty-state legal survey. Have counsel confirm the rules that apply before deployment.
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New York City AEDT considerations
For a covered automated employment decision tool (AEDT) used in New York City, the Department of Consumer and Worker Protection (DCWP) says an independent bias audit must be conducted within one year of use, audit information must be publicly available, and required notices must be provided. The city code specifies notice at least 10 business days before use, including that an AEDT will be used and the job qualifications and characteristics it will assess. It also addresses making information about data types, sources, and retention policy available as specified.
DCWP identifies July 5, 2023 as the date enforcement of Local Law 144 and its rule began; that is a historical enforcement date, not a recurring deadline. Ask the vendor for the exact audited version and audit scope, then independently confirm whether your tool and use are covered and whether your organization has met its obligations. An audit or vendor assurance does not itself establish compliance for every employer or deployment.
6. Compare vendors on evidence you can verify
Set priorities based on the feature’s use, the people affected, and the risks identified in your pilot. NIST notes that trustworthiness characteristics can involve tradeoffs; no single score is a universal measure of a hiring tool. The scorecard below is a practical comparison aid, not an official NIST checklist.
| Evaluation area | Evidence or test to request |
|---|---|
| Job-related validity | Validation for the intended task, role, outcome, and applicant context; definitions for claimed constructs such as “fit” or “quality.” |
| Reliability and limits | Error rates, uncertainty, robustness, known failure conditions, and operating limits. |
| Fairness and accessibility | Relevant testing, accommodation handling, accessible alternatives, and corrective actions when issues are found. |
| Transparency and control | Usable explanations of outputs, recruiter review and override options, and records sufficient to investigate decisions. |
| ATS and data fit | Observed data mapping, permissions, duplicate handling, logs, retention, deletion, and export behavior in your intended workflow. |
| Security and privacy | Current vendor documentation for data protection, subprocessors, access, retention, and restrictions on secondary use. |
| Operations | Monitoring, change notices, support, implementation requirements, fallback behavior, and incident response. |
| Total cost | Full costs for setup, integration, usage, audit work, and continuing oversight—not only the quoted license. |
7. Set ownership and monitoring before launch
Name an internal owner for the feature and define who can review or override its outputs. Specify which events trigger investigation, retesting, suspension, or a return to the existing ATS workflow—for example, a material model change, unexpected failure pattern, or operation outside validated limits. Decide what evidence will be reviewed after launch, how often it will be revisited, and how affected teams will be told about changes.
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Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minuteKeep the decision boundary explicit: the vendor supplies a tool, but your organization is responsible for deciding whether and how it is used in its hiring process. The NIST AI Risk Management Framework provides a voluntary lifecycle structure for identifying, assessing, managing, and monitoring AI risks; it does not replace jurisdiction-specific legal advice.
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