Policy Template: Disclosing AI Use in Hiring and Performance Decisions

Stop losing trust — tell people when AI shapes hiring or performance

Finding and keeping talent already drains your time and cash. When your HR tech quietly uses AI to screen candidates or score performance, you risk legal exposure, disengaged employees, and bias that silently erodes hiring quality. In 2026, buyers and ops leaders must move beyond ad hoc disclosure: adopt a clear, defensible AI disclosure policy that protects your business and builds trust with candidates and staff.

Why AI disclosure matters now (2026 context)

Late 2025 and early 2026 brought two decisive shifts: regulators and industry frameworks sharpened rules for automated decision-making, and workers—empowered by transparency expectations—demand to know when AI affects hiring or evaluation. Whether you're a 10-person startup or a 500-person firm, a documented AI disclosure policy is no longer optional. It supports compliance, employee retention, and fair hiring practices while preserving productivity gains from automation.

Major governance efforts since 2024—updated AI risk guidance from national standards bodies, new requirements in the EU and several U.S. states, and employer-focused best practices—emphasize three priorities: transparency, human oversight, and demonstrable bias mitigation. Implementing a policy now prevents costly retrofits and reputational risk.

Core principles every AI disclosure policy must include

• Transparency: Communicate clearly when AI is used, what it does, and what data it uses.
• Purpose limitation: Specify whether AI supports screening, ranking, interview scheduling, or performance scoring.
• Human oversight: Define who reviews automated decisions and how appeals work.
• Data minimization: Use only data necessary for the decision and state retention periods.
• Explainability: Offer accessible explanations for decisions and the factors considered.
• Consent and notification: Where required by law or best practice, obtain consent or provide clear notice.
• Vendor transparency: Require vendors to provide model documentation, testing results, and update logs.
• Auditability: Keep records of models, versions, data sources, and evaluation metrics for at least the retention period specified.

Ready-to-use policy template: Disclosing AI Use in Hiring and Performance Decisions

Copy, customize, and integrate this template into your employee handbook, ATS settings, and offer letters. Replace text in [brackets] with your organization’s details.

Policy Title

[Company Name] Policy: Disclosure of AI Use in Hiring and Performance Decisions

Purpose

This policy explains how [Company Name] uses automated tools and artificial intelligence (AI) in hiring, promotion, and performance evaluation processes, how affected individuals will be informed, and the safeguards in place to ensure fairness, transparency, and accountability.

Scope

This policy applies to all candidates, employees, contractors, and vendors involved in employment decision-making across [Company Name] and its subsidiaries.

Definitions

• AI / Automated Decision-Making (ADM): Any software or model that processes data to make, assist, or recommend hiring or performance decisions.
• Vendor-supplied model: An ADM system provided and maintained by a third party.
• Human reviewer: An appropriately trained staff member who verifies or overrides ADM outputs.

Policy Statements

1. Disclosure: We will notify candidates and employees when AI is used to screen, score, rank, or otherwise materially influence hiring, promotion, or performance decisions. Notice will be proportional and accessible.
2. Purpose and data: Notices will state the purpose of the AI (e.g., résumé screening, video interview analysis, performance trend scoring) and the categories of personal data used.
3. Human oversight: All final hiring and disciplinary decisions will be subject to human review. The policy will name the role responsible for oversight and appeal handling.
4. Explainability & appeal: Affected individuals may request an explanation of how AI influenced their outcome and may appeal decisions through the process in Section [Appeals].
5. Vendor management: Contracted providers must supply model documentation (model card), fairness and robustness test results, data provenance, and update logs before procurement and at each significant version change.
6. Data retention & security: Records of automated decisions, model versions, and test results will be retained for [X] years and stored under our data retention and security policies.
7. Training & accountability: HR and hiring managers will receive training on AI tool limitations, bias mitigation, and how to apply human judgment.

Notification & Consent

We will provide notice at the earliest reasonable point (e.g., job posting, application submission, interview scheduling, or performance review). Where required by law or risk level (see Risk Assessment), we will obtain explicit consent prior to using AI tools.

Appeals

Candidates and employees impacted by AI-assisted decisions may request a review by contacting [HR Contact] within [X] days. The review will be completed within [Y] business days and include access to a human reviewer and, where feasible, a reasonable explanation of the factors used. Maintain an audit trail and incident comms plan similar to a postmortem for transparency and follow-up.

Governance & Audit

All AI systems used in hiring and performance will be subject to periodic audits for accuracy, fairness, and drift. Audits will be maintained by [AI Governance Owner] and reported to senior leadership.

Enforcement

Violations of this policy may result in corrective action, up to and including termination. Vendors who fail to comply may have contracts paused or terminated.

Effective Date & Review

This policy is effective [Date] and will be reviewed at least annually or after any material change to AI systems.

Practical notification samples — tailored to where people see them

Use these short texts verbatim in job postings, ATS workflows, or email: concise, compliant, and human-friendly.

1) Job posting / ATS (short)

"We use automated tools to help screen applications. If our systems are used in your application, we will notify you and provide a way to request human review. Learn more: [link to policy]."

2) Application confirmation email (medium)

"Thank you for applying to [Company]. Parts of our hiring process use automated tools to review qualifications and schedule interviews. If our systems influence your application, you may request an explanation or human review. View details: [link]."

3) Performance review notification (employee-facing)

"As part of our performance program, we use analytics and AI to identify development priorities. These tools do not make final decisions—your manager will discuss any outcomes and you may request an explanation or appeal through HR. See: [link]."

How to implement the policy — step-by-step (timeline for first 90 days)

1. Inventory (Days 1–14): List all tools that touch hiring or performance data—ATS filters, video interview scoring, sentiment analysis, scheduling bots, performance analytics.
2. Risk assessment (Days 7–21): For each tool, categorize impact (low/medium/high). High-impact tools require prior notice and often consent.
3. Vendor documentation (Days 10–30): Request model cards, testing reports, and data provenance from vendors. If unavailable, flag as a procurement risk.
4. Draft notice texts (Days 14–28): Create job-posting and email templates and publish the full policy on your website and intranet.
5. Update processes (Days 21–45): Integrate notices into ATS workflows and offer letters; add a checkbox or link if consent is required.
6. Train HR and managers (Days 30–60): Run workshops on human oversight, bias checks, and handling appeals.
7. Audit framework (Days 45–90): Define metrics (see checklist), logging procedures, and audit cadence; schedule the first audit at 90 days.
8. Communicate (Day 60): Launch internal communications explaining why disclosure helps candidates and employees and how appeals work.

Compliance & governance checklist (2026 updates)

Use this checklist to validate your implementation against 2026 best practices and regulatory signals.

• Published AI disclosure policy and public link.
• Inventory of AI/ADM tools with risk classification.
• Model documentation (model cards) for each tool.
• Fairness testing results and subgroup performance breakdowns.
• Human oversight roles and documented review workflows.
• Appeal process with SLA for responses.
• Retention schedule for decision logs and audit evidence.
• Vendor contract clauses for transparency, testing, and notification of model changes.
• Training records for HR and hiring managers.
• Quarterly audit schedule and remediation tracking.

Metrics to track — what auditors will ask for

• Disparate impact ratios across protected classes (hiring pass rates, interview invites, etc.).
• False negative / false positive rates for screening models.
• Appeal volumes and outcomes—how often human review changes an automated result.
• Model drift signals (performance degradation over time).
• Time-to-hire and candidate satisfaction metrics before and after AI deployment.

Real-world example: How one small retailer implemented disclosure

A 120-person retail chain introduced an AI résumé screener and sentiment analysis for internal promotion in 2025. They built a short disclosure in job postings and a simple appeal path to HR. Within six months they reported:

• 20% faster time-to-interview;
• Appeals reduced hiring errors by 12% after human review caught misclassifications;
• Candidate Net Promoter Score improved when the company added a short explainer page about how AI was used.

This case shows disclosure + human oversight preserves productivity gains while lowering legal and engagement risk.

Common questions (and short, practical answers)

Do we always need consent?

Not always. Many jurisdictions accept notice for low-risk tools. For high-risk decisions (automated denials, high-stakes performance actions) or where local law requires it, obtain explicit consent. Treat consent as a risk management tool, not an ethical shortcut.

How detailed must the explanation be?

Provide a clear, accessible summary that explains purpose, data types, and human oversight. Preserve technical model documentation in internal records for audits and deep-dive requests. Publish condensed model cards and store full documentation internally; see guidance on versioning and model governance.

What if a vendor refuses to share model details?

Escalate procurement: require a minimum of model cards and fairness test results in contracts. If the vendor refuses, either negotiate stronger contractual assurances or consider alternatives. Use the case study template approach to document procurement risk and remediation steps.

Advanced strategies & future-proofing (2026+)

As AI shifts from tactical execution to broader workforce decisions, adopt these advanced practices:

• Model cards & datasheets: Publish summarized model cards internally and for candidates where feasible. See the governance playbook for examples.
• Version control: Track model versions and mark which version produced a given decision.
• Privacy-enhancing tech: Use differential privacy or federated approaches for sensitive input data.
• Shift-left testing: Run fairness and robustness tests during procurement, not after deployment.
• Continuous learning guardrails: If models update automatically, enforce canary testing and rollback mechanisms.
• Cross-functional governance: Combine HR, legal, data science, and operations to review tools quarterly. Consider a rapid response and audit playbook similar to engineering incident comms (postmortem templates).

Transparency builds trust; trust preserves talent. A clear disclosure policy turns compliance into a competitive advantage.

Actionable takeaways — implementable in 7 days

1. Publish a short AI notice on job postings and the careers page today.
2. Send an internal memo naming the AI governance owner and appeal contact.
3. Request model cards and fairness test results from your top two vendors this week.
4. Add a human-review step to any high-impact automated decision workflow.
5. Schedule a 60-minute training for hiring managers on evaluating AI outputs. For a training playbook, see Gemini guided learning approaches.

Downloadable next steps & templates

Use the template above as your master policy. To operationalize quickly, create three deliverables: (1) a job-posting notice, (2) an ATS confirmation email, and (3) an employee performance notice. Store audit logs and appeals in your HRIS. If you need a ready-made package with vendor contract clauses, sample SLA language, or an audit workbook, consider downloading our full HR AI Governance Kit or using the case study template format to standardize vendor requirements.

Call to action

Protect hiring outcomes and employee trust by adopting a clear AI disclosure policy today. Download the editable policy and customer-facing notices, or contact our HR compliance team for a tailored AI governance audit and implementation plan.

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