The Tool Desk
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Start by defining what the AI feature does
“AI recruiting software” can describe tools with very different effects on candidates. A summarizer that helps a recruiter organize notes is not the same as a tool that scores applicants or ranks them for advancement. Describe the feature’s function and decision boundary before evaluating its claims.
Document the use case
Record whether the feature sources candidates, parses resumes, recommends or ranks applicants, assesses interviews, generates candidate communications, or summarizes information for recruiters. Specify whether it only reduces administrative work or can influence who advances, is suppressed, or is rejected. Include the intended users, affected job families, candidate locations, languages, and point at which a human makes the decision.
Ask the vendor to explain:
- Its intended purpose, unsupported uses, and what output it produces.
- Which inputs it uses, including inferred or derived traits, and how recruiters should interpret the output.
- How the feature can influence decisions: can it recommend, rank, suppress, or reject a candidate?
- What model, data-source, or product changes require retesting or customer notification?
Do not rely on a feature’s name or the vendor’s description alone to decide whether it materially supports selection. In New York City, for example, whether a tool falls under the automated employment decision tool (AEDT) rules depends on its function and impact, not simply its product label. The New York City Department of Consumer and Worker Protection (DCWP) and the city code provide the relevant local definitions and requirements.
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Ask for evidence that fits your hiring task
A general accuracy claim or polished dashboard does not show that a tool is suitable for a particular job, outcome, or applicant population. Ask for a validation package that identifies what the tool is intended to measure and how its performance was evaluated in a relevant context.
Check the claim and its limits
If a vendor says a feature predicts “quality,” “fit,” or “potential,” ask for an operational definition: what observable outcome stands for that concept, and why is it job-related? Ask for the validation design, sample, roles and operating conditions studied, metrics and uncertainty, subgroup analysis, known confounds, and limits on applying the results to other jobs or populations.
NIST’s AI Risk Management Framework Playbook recommends documenting construct, internal, and external validity, reliability, robustness, assumptions, and operational limits. It also cautions that proxy measures can encode confounding or spurious associations. A vendor’s evidence should therefore explain not only the score but also what it does not establish.
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Run a relevant pilot
Use a controlled pilot that reflects the roles and workflow you expect to deploy. Compare the AI-supported process with the current process and review a suitable human-assessed sample. Examine false negatives (qualified candidates the tool misses), false positives, recruiter override rates, and downstream outcomes where lawful and appropriate. Where governance and privacy controls permit, examine variation by role, location, language, and relevant groups.
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These checks help expose context-specific problems; no single metric or threshold guarantees fairness or job-related validity. Agree in advance on what evidence is sufficient for your use case, what findings require remediation, and who can pause or stop the pilot.
Test how it behaves inside your ATS
An integration is more than a connector. In a representative sandbox or controlled pilot, follow candidate and job records through the complete workflow—from the ATS to the AI feature and back—and test both ordinary use and failure conditions.
Verify data, access, and auditability
- Inspect which fields are sent and returned, how they are mapped, and whether every field is needed.
- Test identity matching, duplicate records, recruiter permissions, and who can see AI-generated scores or recommendations.
- Confirm whether the system logs inputs, outputs, source data, prompts where applicable, recruiter reviews and overrides, and model or version changes.
- Ask about retention, deletion, exports, subprocessors, and restrictions on using customer data to train models.
- Check how and when the vendor notifies you about model updates, changed data sources, or feature retirement.
Exercise errors and fallback paths
Test latency, failed requests, retries, outages, and malformed or missing data. Confirm the ATS workflow can safely fall back to its existing process rather than silently dropping candidates or treating a missing score as a negative result. Define what recruiters should see when the feature is unavailable and how incidents are recorded and escalated.
NIST’s Playbook identifies unit, integration, functional, and other software tests as useful approaches. It also recommends defining operating limits, monitoring performance, and deciding what to do when a system operates outside its validated range. The available sources do not establish the integrations or performance of any particular recruiting-software vendor, so verify these behaviors in your own environment.
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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchEvaluate accessibility and disability safeguards
Ask the vendor to identify barriers the tool may create for people with different disabilities and demonstrate the candidate experience, not just the recruiter interface. Confirm how accommodation requests are routed, who can pause an automated step, and what accessible alternative assessment path is available.
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The EEOC and Department of Justice (DOJ) explain that AI tools can screen out applicants with disabilities. Their guidance highlights three concerns: having a process for accommodation requests; avoiding screening out a person who could perform the job with an accommodation; and avoiding tools that elicit disability or medical information. Train recruiters and hiring teams on the escalation path so an accommodation request does not get lost in an automated workflow.
Map legal obligations to the tool and the candidate’s location
For US employers, requirements can depend on the feature’s function and the locations of the employer and candidates. Consider applicable federal, state, and local employment, disability, privacy, and automated-decision rules. This overview is not a complete fifty-state legal survey; confirm the requirements for your actual deployment with counsel.
Check whether New York City Local Law 144 applies
For covered AEDTs used to screen candidates or employees for employment decisions, NYC 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 calls for notice at least 10 business days before use and describes notice that an AEDT will be used and the job qualifications and characteristics it will assess. It also describes making information about data type, source, and retention policy available as specified by the code.
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Ask the vendor for the exact audited version and audit scope, then independently assess whether your tool and use are covered and whether your own deployment meets the requirements. DCWP states that enforcement of Local Law 144 and its rule began July 5, 2023; that date is not a recurring deadline or a substitute for checking current requirements.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Compare vendors against decision-relevant criteria
Set the importance of each criterion according to the feature’s role in the hiring decision and the people affected. NIST notes that trustworthiness characteristics can involve tradeoffs, so a single universal score is not a substitute for context-specific priorities. The table below is a practical procurement framework, not an official NIST checklist.
| Criterion | Evidence or capability to request | What a weak answer may signal |
|---|---|---|
| Job-related validity | Evidence tied to the claimed outcome, relevant roles, applicant context, and intended use; a clear account of the construct being measured. | A broad “quality” or “fit” claim without an operational definition or relevant validation. |
| Reliability and limits | Performance variation, uncertainty, robustness, known failure conditions, and limits on generalizing results. | A single accuracy number with no explanation of sample, conditions, or limitations. |
| Fairness and accessibility | Testing approach, accommodation process, accessible alternatives, and steps for correcting harmful outputs. | No workable accommodation route or no evidence that candidate-facing steps have been considered. |
| Transparency and control | Interpretable outputs, meaningful recruiter review and override, and logs that support investigation. | Recruiters cannot understand or challenge the output, or the workflow hides how it affected a decision. |
| ATS and data fit | Field mapping, permissions, failure behavior, audit logs, retention, deletion, and export options demonstrated in a pilot. | Integration assurances that have not been tested against your workflows and data. |
| Security and privacy | Current vendor documentation for data protection, subprocessors, retention, and secondary use. | Unclear data handling or no clear restrictions on reuse of customer data. |
| Operations | Monitoring, change notices, support, implementation responsibilities, and incident response. | No owner or process for model changes, out-of-range performance, or incidents. |
| Economics | Total expected costs, including setup, integration, usage, audit work, and ongoing governance. | A price comparison that omits implementation and continuing oversight costs. |
Set launch and ongoing-review gates
Before deployment, assign an internal owner and write down how the system will be used, who reviews its output, and what evidence or incident can trigger a pause. Establish how recruiters can challenge or override an output, how affected teams will be informed about changes, and how performance and disparate effects will be revisited. NIST’s voluntary AI Risk Management Framework provides a lifecycle structure for identifying, assessing, and managing risk; NIST states that AI RMF 1.0 is being revised, so check the current framework when planning procurement.
A launch decision should depend on evidence for the intended workflow, a successful ATS pilot, workable accessibility and human-review controls, a monitoring and incident plan, and a legal review for relevant jurisdictions. If the vendor cannot show how the tool was validated, what it does to candidate decisions, or how you can detect and correct failures, do not treat a successful demo as a reason to deploy it.
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