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To evaluate AI recruiting software for your ATS, test the feature against the hiring task it will actually perform—not the vendor’s AI label or a general accuracy claim. Define how its output could affect candidates, ask for evidence that it works for your jobs and applicant populations, and pilot its data flows, accessibility, human controls, and failure handling before launch. Then check the legal requirements that apply where the tool is used.
How to evaluate AI recruiting software for your ATS: define the decision first
Start by documenting what the feature does and where it sits in the hiring workflow. A tool that drafts routine messages has a different decision role from one that ranks applicants or recommends who advances. The product name is not enough to establish how a feature functions or which rules apply.
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Describe the task and its decision boundary
- Task: Does it source candidates, parse resumes, summarize applications, recommend or rank applicants, assess interviews, generate communications, or perform another function?
- Effect: Does it only reduce administrative work, or can it influence who is reviewed, advances, or is screened out?
- Scope: Record the intended users, affected candidates, job families, locations, languages, and points where a human makes a decision.
Ask the vendor to explain the feature
- What is the intended purpose, and which uses are unsupported?
- What data goes in, including inferred or derived traits, and what output comes back?
- How should recruiters interpret that output, and which decisions may it influence?
- Can it reject, suppress, or rank a candidate? Can a recruiter inspect and change its effect?
- What changes to the model, inputs, or data sources require retesting or customer notification?
Record the answers in a use-case specification. It gives your team a concrete basis for assessing evidence, setting pilot boundaries, and deciding whether the feature’s actual behavior matches its stated purpose.
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Ask for evidence that matches your jobs and applicant population
A vendor’s performance claim is useful only if you can tell what was measured, against what criterion, for which roles and people, and under what conditions. If the vendor says a system predicts “quality,” “fit,” or “potential,” ask for an operational definition and evidence that the chosen proxy measures that concept in the hiring context where you plan to use it.
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Request a validation package
Ask the vendor to identify the target construct and criterion, sample and job context, evaluation design, metrics, uncertainty, subgroup analysis, known confounds, and limits on generalizing the results. Also ask how reliability, robustness, assumptions, and operating limits are documented. NIST’s AI Risk Management Framework Playbook recommends documenting these kinds of validity and reliability considerations; NIST cautions that unvalidated systems may be inaccurate or unreliable and that proxy measures can encode confounding or spurious associations.
Do not treat a polished dashboard, a single accuracy number, or performance on a different job family as proof of fit. The sources available here establish no recruiting-software vendor performance statistic or comparative effectiveness result, and no single metric or threshold guarantees fairness.
Run a controlled, human-reviewed pilot
Use representative roles and a defined pilot scope. Compare the AI-supported workflow with the existing process and review a human-checked sample. Where lawful and appropriate, examine false negatives—qualified candidates the system misses—false positives, recruiter override rates, and downstream outcomes. Break results down by role, location, language, and relevant groups only with suitable governance and privacy controls.
Use pilot findings to identify where the feature works, where it fails, and what conditions fall outside the evidence. Decide in advance what evidence would support expansion, what would require remediation, and what would lead you to stop using the feature; do not invent a passing score simply because a vendor reports one.
Test the ATS integration as a workflow, not just a connector
Run a representative sandbox or controlled pilot and trace records through the complete process. A successful connection alone does not show whether fields, permissions, errors, or AI outputs behave safely in recruiters’ day-to-day work. NIST’s Playbook recommends software and integration testing and documenting system limits.
Verify data, access, and failure behavior
- Inspect the candidate and job fields sent to and returned from the feature, field mapping, and data minimization.
- Test identity matching, duplicate records, recruiter permissions, and who can see scores or recommendations.
- Simulate latency, failed requests, retries, and outages. Confirm there is a safe fallback to the existing ATS workflow.
- Check whether outputs, prompts, source data, recruiter reviews, overrides, and model or version changes are logged.
- Confirm retention, deletion, exports, subprocessors, and restrictions on using customer data to train models.
- Ask how the vendor notifies you about model updates, changed data sources, or a feature’s retirement.
Document what should happen when the feature is unavailable or produces an output outside its validated range. The ATS should not quietly turn a technical failure into an unreviewed candidate decision.
Check accessibility and disability safeguards
Ask the vendor to demonstrate candidate-facing steps and explain how the system may create barriers for people with different disabilities. Confirm that candidates can request an accommodation, that the request reaches the responsible team, and that recruiters know how to pause an automated step while it is handled.
Require an accessible alternative assessment path and a way for recruiters to review or correct problematic outputs. Consider whether a timed assessment, video interview, or other selection step could screen out a person with a disability who could perform the job with an accommodation, or elicit disability or medical information. The EEOC and Department of Justice explain that AI tools can screen out applicants with disabilities and identify accommodation processes, exclusion despite ability to do the job with accommodation, and elicitation of disability or medical information as concerns. As EEOC Chair Charlotte A. Burrows put it in a May 12, 2022 release, “New technologies should not become new ways to discriminate.”
Map legal obligations to the tool’s use and location
For US deployments, check applicable federal, state, and local employment, disability, privacy, and automated-decision requirements based on the feature’s function and candidate location. The sources cited here are not a complete fifty-state legal survey. NIST’s AI Risk Management Framework is voluntary; it is a risk-management resource, not a substitute for legal review.
For covered NYC AEDTs, confirm the specific requirements
New York City Local Law 144 applies to covered automated employment decision tools used to screen candidates or employees for employment decisions. NYC’s Department of Consumer and Worker Protection says covered use requires a bias audit within one year, publicly available audit information, and required notices. The city code specifies notice at least 10 business days before use, including notice that an AEDT will be used and the job qualifications and characteristics it will use; it also describes making information about data type, source, 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 current deadline. Ask for the audited tool version and audit scope, then have your organization independently confirm whether the feature and your use are covered and meet applicable obligations. Do not assume NYC requirements apply elsewhere or to every ATS feature.
Compare vendors on evidence that matters to your use case
Use a scorecard built around your documented task and risk. Set priorities before demonstrations; a strong result on one dimension does not settle weaknesses on another. NIST notes that trustworthiness characteristics can involve tradeoffs. The criteria below are a procurement framework, not an official NIST checklist.
Best Value
| Evaluation area | Evidence or demonstration to request | Decision question |
|---|---|---|
| Job-related validity | Validation for the relevant roles, outcome, and applicant context; definition of the target construct and criterion | Does the evidence support the task we intend to use it for? |
| Reliability and limits | Error patterns, uncertainty, robustness, known failure conditions, and operating limits | Can we recognize when the output is unreliable or outside scope? |
| Fairness and accessibility | Testing approach, accommodation routing, alternative paths, and corrective actions | Can affected candidates access a fair process, and can problems be addressed? |
| Transparency and control | Output interpretation, human review, override behavior, and audit logs | Can recruiters understand, challenge, and document the feature’s influence? |
| ATS and data fit | Field mapping, permissions, logs, retention, deletion, exports, and failure handling | Does it work safely in our actual workflow and data environment? |
| Security and privacy | Current vendor documentation on data protection, subprocessors, retention, and secondary use | Are data handling and contractual controls acceptable for our organization? |
| Operations | Change notices, monitoring, support, implementation effort, and incident response | Can we detect and manage material changes or problems after launch? |
| Economics | Full costs for setup, integration, usage, audit work, and ongoing governance | Does the total cost fit the value and oversight the use case requires? |
For each area, record the evidence, unresolved questions, owner, and decision. Verify current security and privacy claims in vendor materials and contract terms rather than inferring them from a product demo.
Assign ownership and define post-launch monitoring
Before launch, name the business owner, the people authorized to review or override outputs, and the team responsible for accommodation requests, incidents, and legal review. Define which events trigger investigation or suspension, how recruiters are told about changes, and what records are retained for review.
Set a monitoring cadence and revisit performance and disparate effects in the roles and populations where the feature is used, subject to appropriate legal and privacy controls. NIST’s Playbook recommends monitoring performance, watching for operation outside defined limits, and deciding in advance what actions follow an alert. The NIST AI RMF 1.0 was developed with more than 240 contributors across private industry, academia, civil society, and government, according to NIST; that contributor count describes the framework’s development, not hiring outcomes. NIST says the framework is being revised, so check the current version when applying it.
Make the procurement decision a documented gate
Approve a feature only when its intended role is clear, the evidence supports that role in your context, the pilot exposes manageable errors and integration behavior, accessibility and legal safeguards are addressed, and named owners can monitor it after deployment. If a vendor cannot explain what the system measures, where its limits are, or how a human can intervene, pause procurement rather than treating the gap as a configuration detail.
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