The AI recruiting governance checklist: What TA leaders need before they expand AI use
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- AI governance starts before a tool reaches candidates, with clear rules for what AI can do, who owns decisions and how outputs are reviewed.
- Evaluate existing tools with the same rigor as new vendors, including data use, explainability, bias testing, certifications and human oversight.
- Keep governance active. Regulations, vendor capabilities and candidate expectations change, so policies, audits and documentation need regular review.
AI is entering nearly every stage of recruiting, from sourcing and scheduling to screening, interviewing and reporting. Governance needs to move just as intentionally.
As candidate expectations and regulatory scrutiny increase, hiring teams need to be clear about where AI is used and how people stay accountable for decisions. Legal and executive teams also need confidence that those choices are documented and explainable.
That makes governance something to design from the start. Before AI touches a candidate, talent acquisition (TA) leaders should define what it can do, where human judgment is required and what evidence the organization can provide about how the system works.
This AI recruiting governance checklist gives hiring teams a practical framework for putting those expectations in place before expanding their use of AI.
Why AI recruiting governance cannot wait
In 2026, the expectations around AI in hiring are becoming much clearer. Regulators are defining new requirements, courts are testing where responsibility sits and candidates increasingly expect employers to explain how AI shapes the process. Governance gives hiring teams a way to respond with clear rules, documentation and human accountability.
The regulatory picture is also getting more specific. Under the EU AI Act, many AI systems used in recruitment and candidate selection are considered high-risk, which brings requirements around areas such as risk management, documentation, transparency and human oversight.
In the U.S., requirements vary by location:
- New York City’s Local Law 144 requires automated employment decision tools to undergo a bias audit, publish audit information and provide required notices to candidates or employees.
- Illinois requires notice, information about how AI video interview tools work and candidate consent before use.
- Colorado’s updated law introduces requirements for automated decision-making technology used in consequential decisions, including employment, beginning January 1, 2027.
Court cases are shaping the boundaries, too. Mobley v. Workday has continued through 2026 with discrimination claims tied to automated applicant screening. The case puts a sharper focus on where responsibility sits when a vendor’s technology materially influences an employer’s screening decisions.
Standards are evolving alongside regulation. ISO/IEC 42001, the world’s first AI management system standard, gives organizations a structured framework for managing AI risk, transparency and accountability.
The Greenhouse 2026 AI in Hiring Report also shows trust eroding on both ends of the hiring process. 46% of candidates do not trust the hiring process, while 74% of hiring managers say they are more worried about fake credentials than a year ago. Trust is eroding for candidates and hiring teams alike, at the exact moment AI adoption is accelerating.
Taken together, these shifts point in the same direction: hiring teams need a clear view of what AI does, where people stay accountable and how those decisions are documented. Strong governance creates that structure necessary as AI use expands further.
What AI recruiting governance actually covers
AI recruiting governance gives hiring teams a clear operating model for how AI is used, who owns the decisions around it and what evidence the organization can produce when questions come up.
A practical framework should cover five areas:
- Policy: Define what AI is allowed to do in the hiring process, where its role ends and which decisions stay with people.
- People: Clarify who owns the program, who signs off on new use cases and who needs to be part of key decisions.
- Process: Set a consistent approach for evaluating, launching, adjusting and retiring AI capabilities.
- Documentation: Keep the records you may need to share with a regulator, board, auditor or candidate, including approvals, testing, controls and disclosures.
- Monitoring: Check how systems perform over time, watch for changes in accuracy and consistency and make sure outcomes continue to match your hiring standards.
A responsible AI recruiting checklist turns those areas into repeatable practice. Your AI recruiting policy should give recruiting, legal, information security and hiring leaders a shared set of expectations they can apply consistently.
AI recruiting governance checklist
A strong governance program turns principles into repeatable decisions. Use this checklist to document how AI works, keep people accountable and review performance over time.
Policy, vendors and documentation
Start by getting a clear picture of how each AI tool works and where it sits in your hiring process:
- Document every AI use case in your hiring process
- Record what each tool does, what data it uses, what outputs it produces and where candidates encounter it
- Define the boundaries of each use case, including which variables the AI considers and where human review begins
- Check whether the AI operates within a structured hiring framework using role-relevant criteria
- Confirm whether candidate or customer data is used to train vendor models
- Review how the vendor explains candidate scores, categories or other outputs
- Ask how often independent bias audits are conducted and which protected classes they cover
- Review relevant certifications, including ISO 27001, ISO 27701 and ISO/IEC 42001
- Confirm whether administrators can turn AI features on or off
- Check whether the tool produces reviewable drafts or takes action automatically
- Apply the same review to AI already in your hiring stack
- Keep training plans, candidate-facing language, legal approval and information security approval with each documented use case
- Get legal and information security sign-off before launch
The same review should extend to AI already in your hiring stack. Greenhouse AI in Hiring research found that only 21% of U.S. recruiters are very confident their AI screening systems are well tuned. Another 25% aren’t confident those systems are keeping qualified candidates in consideration, while 8% don’t know what their AI prioritizes when screening.
By reviewing how AI is used across your hiring process, you can close those visibility gaps and keep governance grounded in how the technology actually works.
Human oversight and candidate transparency
Once you understand how the technology works, define where people stay responsible for decisions and what candidates need to know:
- Keep selection, evaluation, interview scoring and final hiring decisions with people
- Use AI to inform, summarize and surface relevant information while keeping final judgment with the hiring team
- Use lower-risk applications such as scheduling, offer workflows, interview note-taking and high-volume pipeline triage where they support the team
- Add human review before AI-supported screening, ranking, rejection communications or other candidate-facing outputs affect a candidate
- Tell candidates in plain language where AI is used and what role it plays
- Share that information at the point candidates encounter the AI-enabled step
- Explain how people stay involved in the decision process
- Offer an opt-out or manual review path where appropriate
- Prepare recruiters to answer questions about what the AI evaluates, which data it uses and who makes the final decision
- Keep claims about bias, fairness and performance grounded in evidence
These checks ensure transparency aligns closely with what candidates say they want from employers. Greenhouse AI in Hiring research found that 87% of candidates say companies should disclose AI use in hiring, while 68% describe that disclosure as very important or critical to their decision to apply.
Clear explanations of where AI is used and how people stay involved can help candidates better understand the process and the decisions that affect them.
Monitoring, audits and regulatory alignment
Governance continues after a tool is approved. Use these checks to review how AI performs over time and keep your approach aligned with changing requirements:
- Set a review cadence based on the risk of each AI use case
- Review higher-impact features, such as candidate matching, screening and AI interviewing, more frequently
- Use monthly bias reviews for higher-risk features where appropriate
- Review lower-risk uses at least quarterly or on another cadence that matches your legal and governance requirements
- Review every active AI feature at least once a year
- Refresh your AI policy annually as regulations and use cases change
- Track changes in AI accuracy, consistency and hiring outcomes over time
- Review candidate feedback about AI-enabled experiences
- Keep bias audits, impact assessments and approvals easy to access
- Update candidate disclosures when the role of AI changes
- Confirm legal requirements in every jurisdiction where you hire
- Track major frameworks that affect hiring AI, including the EU AI Act, NYC Local Law 144, the Colorado AI Act and the Illinois AI Video Interview Act
- Treat ISO/IEC 42001 as an AI management standard for vendor evaluation, not as a legal requirement
The goal is to know what each AI system does, keep people accountable for hiring decisions and revisit those controls as the technology, use cases and legal requirements change.
How Greenhouse operationalizes AI recruiting governance
Our approach starts with five public AI principles that guide how AI is designed and used across Greenhouse:
- Structured hiring is at the core: AI works within defined criteria and an evidence-based hiring process.
- Hiring, reimagined: AI should help teams improve how hiring works, not simply automate existing steps.
- Grounded in the human experience: Technology should support how people actually make decisions.
- Decision ownership is explicit: AI can inform and summarize, while people remain responsible for decisions.
- Explainability is non-negotiable: Outputs should be understandable and grounded in observable signals.
Governance is also backed by independent standards. Greenhouse holds ISO 27001, ISO 27701 and ISO/IEC 42001 certifications. The ISO/IEC 42001 audit was conducted by Schellman, providing independent validation of the AI management system.
Greenhouse Talent Matching and Voice AI also undergo independent monthly bias audits from Warden AI across 10 protected classes. Greenhouse publishes information about those audits so customers can review how the systems are tested.
Controls extend into the product as well. Organizations can turn AI features on or off, and Talent Matching supports manual-review requests and more granular controls by office, department or job. Greenhouse also states that customer personal data is not used to train internal large language models, proprietary models or third-party models.
See structured hiring meet responsible AI in Greenhouse
Good AI governance should be visible in the hiring process itself. Clear evaluation criteria give AI useful boundaries. Explainable outputs help hiring teams understand the signal behind an output. Human ownership keeps consequential decisions where they belong.
Greenhouse brings those pieces together inside structured hiring, supported by ISO/IEC 42001 certification, independent monthly bias audits and controls that give organizations more visibility into how AI participates in hiring.
Request a demo to see how Greenhouse puts responsible AI governance into practice.
FAQs
What should be in an AI recruiting governance checklist?
Include clear AI policies, ownership, vendor review criteria, human oversight, candidate disclosures, documentation, bias testing and ongoing monitoring. The checklist should show what each AI tool does, who approves it, who reviews its outputs and how your team responds when the technology or regulatory requirements change.
What is responsible AI in recruiting?
Responsible AI in recruiting uses technology within clear hiring criteria, gives people ownership of consequential decisions and makes AI outputs understandable and reviewable. It also includes appropriate privacy, security, bias testing, candidate transparency and governance controls.
What questions should you ask an AI recruiting vendor?
Ask how the AI uses data, whether customer data trains models, how outputs are explained, which actions require human review and what bias testing is conducted. Also ask about certifications, administrator controls, manual-review options, data retention and how the vendor supports changing regulatory requirements.
What is meaningful human oversight in AI hiring?
Meaningful human oversight means a person has enough information and authority to review, question and change an AI-supported outcome. The reviewer should understand the evidence behind the output and remain responsible for consequential decisions such as advancing, rejecting or hiring a candidate.
Does the EU AI Act apply to recruiting?
Yes, many AI systems used to recruit or select candidates are classified as high-risk under the EU AI Act. Covered systems can face requirements related to risk management, technical documentation, recordkeeping, transparency and human oversight. Organizations should confirm which obligations apply to their specific systems and use cases.
Does Greenhouse use candidate data to train its AI models?
Greenhouse states that it does not use customers’ personal data to train internal large language models, proprietary models or third-party models. Greenhouse proprietary models may use anonymized, de-identified operational data such as job location, scheduling availability and time-to-hire metrics.
