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Use AI to reduce recruiting administration, not to quietly determine who gets a job opportunity. Acknowledgements, scheduling and document organization are good starting points; sourcing, screening, ranking and assessment need job-related evidence, accessibility and bias safeguards, and meaningful human oversight. The legal requirements depend on where and how the tool is used.
Start with the effect the tool has on a candidate
The key distinction is not whether a product is marketed as AI. It is whether the system merely handles a procedural step or influences who is found, evaluated or advanced. A personalized application acknowledgement that does not affect selection is different from a tool that filters résumés or ranks candidates. The European Commission identifies recruitment and selection systems—including some sourcing and matching or ranking uses—as potentially high-risk under the AI Act, depending on intended use and the Act’s scope and exceptions. European Commission guidance on employment uses explains the category.
That distinction is useful for employers even where a specific high-risk classification does not apply: if a recommendation changes which applicants receive human attention, treat it as part of the selection process and assess its effects accordingly.
What to automate—and what needs review
| Task | Practical approach | Why it matters |
|---|---|---|
| Application receipt acknowledgements | Automate, provided the message does not affect selection. | The European Commission distinguishes a narrow, personalized acknowledgement with no influence on selection from recruitment decision uses. Source |
| Scheduling, reminders and document organization | Reasonable candidates for workflow automation; check that errors do not cause applicants to be overlooked. | These are practical low-stakes recommendations, not a blanket legal safe harbor. |
| Extracting candidate-stated skills or searching application materials | Use to help a recruiter retrieve information, then verify it against the candidate’s materials. | Extraction and search can misread or omit information; if they shape advancement, assess them as part of selection. |
| Sourcing, filtering, ranking, fit scores or shortlists | Use only with evidence that the criteria are job-related, the system has been evaluated for the intended use, and outcomes are monitored. | These uses can determine who is considered. The Commission includes recruitment and selection tools among employment uses that may be high-risk. Source |
| Interview or assessment analysis | Require a clear job-related rationale, accessible assessment methods and an accommodation route; do not treat a score as proof of ability. | The EEOC warns that algorithmic tools may screen out people with disabilities who can perform the job with or without reasonable accommodation. EEOC guidance on visual disabilities and the ADA |
| Final rejection, hiring or other selection decision | Keep an accountable, reviewable process in which a qualified person can independently assess the candidate and change the outcome. | This is a recommended governance standard, not a claim that one universal law bans every automated decision. |
Be especially cautious with tools that infer personality, emotion, health or protected traits, or where the employer cannot explain what criterion is measured, validate it for the role, accommodate applicants, examine disparate outcomes or override a bad result. A vendor’s label or general accuracy claim is not a substitute for evidence about the actual job and use.
#1 Best Overall
Human oversight must have real authority
For high-risk systems covered by the EU AI Act, human oversight is more than a reviewer clicking “approve.” Article 14 requires oversight measures that let people understand system limits, spot anomalies and automation bias, interpret outputs, disregard or reverse them, and intervene or stop the system. It also describes the ability to decide not to use the system in a particular situation. See Article 14 of Regulation (EU) 2024/1689.
In practice, a reviewer needs enough information and time to assess the candidate independently. A process is not meaningfully reviewable if the person sees only a score, is expected to accept the ranking, or lacks authority to override the recommendation. Record who reviewed a consequential recommendation, what information was considered and whether the result changed.
Rank #2
Validate the job-related measure and make it accessible
Before using an assessment, identify the specific job requirement it is intended to measure and the evidence that it measures that requirement accurately for the role and the relevant candidate population. Ask whether a lower score could reflect the assessment format rather than a person’s ability to do the job, including with reasonable accommodation.
The EEOC’s guidance gives the example of a visual disability reducing the accuracy of an AI assessment and identifies an alternative test format as a possible accommodation. Employers should make clear how applicants can request an accommodation, who handles the request, and how quickly it can be addressed. An alternative format should assess the same relevant job requirement where feasible, rather than penalize a candidate for disability-related barriers. See the EEOC guidance on visual disabilities in the workplace.
Check outcomes without treating one metric as a guarantee
In the United States, existing federal anti-discrimination duties apply when automated systems make or inform selection decisions. The EEOC explains that Title VII applies to selection procedures using automated systems. Its four-fifths rule is a screening indicator, not proof that a tool is fair: the agency’s FY2023 report says that satisfying the rule does not guarantee a selection procedure will avoid a disparate-impact finding. EEOC Fiscal Year 2023 Agency Financial Report.
Review evidence about selection outcomes across relevant groups in the context of the particular tool, job and decision. Investigate material differences rather than assuming a single threshold resolves them. Also examine whether the data and criteria reflect the role as it exists now: the AI Act’s Recital 67 calls for relevant, sufficiently representative datasets that are as complete and error-free as possible for the intended purpose, with attention to bias and feedback loops. European Commission explanation of Recital 67.
Rank #4
Apply the rules for the location and use
European Union
The AI Act places certain recruitment and selection uses among employment systems that may be high-risk, subject to the system’s intended purpose, the Act’s scope and any applicable exceptions. The Commission states that the relevant high-risk system rules for employment apply from 2 August 2026; that date has passed as of October 2026. Employers should check the current consolidated text and guidance to determine whether a particular system and use are covered. The Commission’s AI Act FAQ and employment guidance provide context, while the Regulation text is the legal source.
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Federal employment discrimination and disability accommodation duties remain relevant when an employer uses software or AI in selection. The EEOC materials explain how existing duties apply and identify risks; they do not establish that every AI hiring use is prohibited. The tool and its role in a decision matter. See the EEOC disability guidance and FY2023 report.
New York City
New York City’s Local Law 144 creates a local regime for covered automated employment decision tools (AEDTs). The city’s Department of Consumer and Worker Protection says covered employers and agencies must ensure a bias audit within one year before use, publish audit information and provide required notices. Coverage and details depend on the employer, tool and use, so check the current local requirements rather than applying them to every employer or location. See the DCWP AEDT program page and its AEDT FAQ.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Questions to ask before deploying a tool
- Purpose: What specific job-related criterion does the tool measure, and how was it validated for this role and intended population?
- Influence: Does it handle a procedural step, or materially affect sourcing, evaluation, ranking or advancement?
- Data: What candidate information does it use, how was it collected, and is it relevant and representative for the intended purpose?
- Access: Can applicants request an accommodation or alternative format, and is there a clear, timely process?
- Outcomes: What evidence exists about selection effects across relevant groups, and what are the limits of the measures used?
- Oversight: Can a trained reviewer understand the recommendation’s basis and limits, independently assess the candidate, and override or stop the system?
- Transparency: Are candidates told about the tool, data use and accommodation routes where applicable rules require notice?
- Lifecycle: Who reviews the system and outcomes, how often, and what changes trigger revalidation?
Compare uses on the same decision factors
When evaluating vendors or proposed uses, compare each option on whether it materially affects selection; job-relatedness and validation evidence; accuracy and accessibility; selection outcomes; data provenance and representativeness; transparency, auditability and recordkeeping; reviewer authority; and jurisdiction-specific notice or audit duties. This is a practical comparison framework synthesized from the sources above, not an official government checklist.
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