Complete AI Hiring Compliance Checklist for 2026: Step-by-Step Guide

2026 marks the most significant expansion of AI hiring regulation in U.S. history. With major laws taking effect in Illinois (HB 3773), Texas (TRAIGA), Colorado (SB 205), plus enhanced California (ADMT) requirements and ongoing NYC Local Law 144compliance, employers need a comprehensive roadmap to navigate this complex regulatory landscape.

This checklist provides detailed, actionable steps to achieve full compliance across all major jurisdictions. Whether you operate in a single state or across the country, this guide helps you prioritize, implement, and maintain compliant AI hiring practices.

Phase 1: Discovery & Assessment (Weeks 1-3)

Begin with a comprehensive audit of your current hiring practices, technology stack, and geographic scope. This foundation determines which requirements apply to your organization and what gaps need addressing.

Step 1.1: Complete AI Tool Inventory

Objective: Identify every system, platform, and tool that uses AI in employment decisions.

What to Document for Each Tool:

• Tool name and vendor: Full product name, vendor name, and version number
• AI functionality: Specific AI features used (scoring, ranking, matching, video analysis, etc.)
• Employment stage: Where in the hiring process the tool is used
• Geographic scope: Which jobs/locations use this tool
• Data inputs: What candidate information the AI analyzes
• Output type: Scores, rankings, recommendations, or automated decisions
• Human review: Level of human oversight in final decisions
• Volume: Number of candidates/employees affected annually
• Biometric data: Whether it collects facial recognition, voice analysis, or other biometrics

Common AI Tools to Review:

• Applicant Tracking Systems: Workday, Greenhouse, Lever, iCIMS, Taleo (check AI features)
• Resume Screening: HireVue Hiring Assistant, SeekOut, Fetcher, Entelo
• Video Interviews: HireVue, Modern Hire, VidCruiter, Spark Hire (facial/voice analysis)
• Assessments: Pymetrics, Criteria, HackerRank, Codility (AI-scored)
• Sourcing: LinkedIn Recruiter, Indeed Smart Sourcing, Hired, ZipRecruiter AI matching
• Background Checks: Checkr, HireRight (AI-powered risk scoring)
• Scheduling: Calendly, GoodTime (usually low-risk)
• Chatbots: Paradox Olivia, Mya, XOR (candidate screening bots)

Actionable Checklist:

• ☐ List all recruitment and HR software subscriptions
• ☐ Interview recruiters and hiring managers about tools they use
• ☐ Review IT/SaaS inventory for hiring-related platforms
• ☐ Check vendor documentation for AI/ML features
• ☐ Identify "shadow IT" tools (unapproved tools managers may be using)
• ☐ Document findings in centralized spreadsheet or database
• ☐ Assign risk rating to each tool (high/medium/low)

Step 1.2: Determine Geographic Scope

Objective: Map which regulations apply to your hiring activities.

Key Questions:

• In which states/cities do you have office locations?
• Where are job postings available (especially remote roles)?
• Where do current employees reside?
• Which jurisdictions could reasonably see your job ads?
• Do you hire independent contractors or gig workers in regulated areas?

Regulation Applicability:

• NYC Local Law 144: Applies to jobs in NYC or candidates residing in NYC
• Illinois HB 3773: Applies to employment decisions affecting Illinois workers
• Texas TRAIGA: Applies to employment in Texas (all employers)
• Colorado SB 205: Applies to employment affecting Colorado residents
• California ADMT: Applies to California employees/applicants (CCPA/CPRA threshold: 100K+ CA residents annually)
• Federal EEOC: Applies to all employers (15+ employees for Title VII)

Actionable Checklist:

• ☐ Create list of all office locations and states
• ☐ Review job board reach (Indeed, LinkedIn, etc.)
• ☐ Identify fully remote positions (highest regulatory risk)
• ☐ Map tools to positions and locations
• ☐ Build compliance matrix: Tool × Location × Regulation
• ☐ Identify highest-risk combinations requiring immediate attention

Step 1.3: Conduct Gap Analysis

Objective: Compare current practices against regulatory requirements and identify deficiencies.

For Each Applicable Jurisdiction, Review:

• Notice/Disclosure: Do you currently notify candidates when AI is used?
• Timing: Is notice provided at the right time (e.g., NYC's 10-day requirement)?
• Content: Does notice include all required elements?
• Bias Audits: (NYC) Do you have current independent audits for all AEDTs?
• Impact Assessments: (Colorado) Have assessments been completed?
• Risk Assessments: (California) Documented for all ADMTs?
• Candidate Rights: Can candidates opt out, appeal, or access AI information?
• Vendor Documentation: Do vendors provide compliance support materials?
• Training: Has staff been trained on compliance requirements?
• Recordkeeping: Are you documenting compliance activities?

Actionable Checklist:

• ☐ Create gap analysis template with all requirements
• ☐ Rate each requirement: Compliant / Partial / Non-Compliant
• ☐ Assign priority: Critical / High / Medium / Low
• ☐ Estimate effort: Hours/days needed to remediate
• ☐ Identify dependencies (e.g., vendor cooperation required)
• ☐ Create remediation project plan with milestones
• ☐ Assign owners for each remediation task
• ☐ Build timeline working backward from effective dates

Phase 2: Bias Audits & Assessments (Weeks 2-6)

This is often the longest phase due to vendor dependencies and audit timelines. Start immediately.

Step 2.1: NYC Bias Audits (Local Law 144)

Requirement: Annual independent bias audit for all Automated Employment Decision Tools (AEDTs).

What Qualifies as an AEDT:

• Substantially assists or replaces discretionary decision-making in hiring or promotion
• Relies on ML, statistical modeling, AI, or data analytics
• Outputs scores, rankings, recommendations, or automated decisions

Audit Requirements:

• Independence: Auditor cannot be involved in tool development/sale
• Methodology: Calculate selection rates by race/ethnicity and sex
• Impact ratios: Compare selection rates to most-selected category
• Recency: Audit must be conducted within 1 year before tool use
• Data quality: Use sufficient test data for statistical significance

Obtaining Audits:

• Option 1 - Vendor-provided: Request from AI vendor (most common for major platforms)
• Option 2 - Employer-commissioned: Hire independent auditor (required if vendor doesn't provide)
• Option 3 - Shared audits: Some audit firms conduct pooled audits for multiple employers using same tool

Actionable Checklist:

• ☐ Identify all AEDTs used for NYC positions
• ☐ Request bias audit from each vendor (allow 4-6 week turnaround)
• ☐ Review vendor audit for completeness and NYC compliance
• ☐ If vendor doesn't provide audit, commission independent auditor
• ☐ Review audit results for concerning disparities
• ☐ If significant bias found, work with vendor to remediate or discontinue tool
• ☐ Prepare public audit summary (required elements)
• ☐ Post audit summary on company website/careers page
• ☐ Set calendar reminder for annual audit renewal (11 months from now)
• ☐ Document audit procurement and review in compliance records

Step 2.2: Colorado Impact Assessments (SB 205)

Requirement: Impact assessment before deploying AI systems affecting Colorado residents.

Assessment Content Requirements:

• Purpose and intended uses of the AI system
• Benefits: How AI improves hiring processes
• Known limitations: What the AI cannot do or may do poorly
• Risks: Potential for discrimination or algorithmic harm
• Transparency measures: How you disclose AI use
• Oversight: Human review and decision-making processes
• Data safeguards: How candidate data is protected
• Training: Staff training on AI use and limitations
• Consumer feedback: Mechanisms for candidates to report concerns or appeal

Actionable Checklist:

• ☐ Create impact assessment template following SB 205 guidance
• ☐ Complete assessment for each AI tool used for CO positions
• ☐ Involve cross-functional team (HR, IT, legal, compliance)
• ☐ Request input from AI vendors on known limitations and risks
• ☐ Document transparency measures (notices, disclosures)
• ☐ Describe human oversight and review procedures
• ☐ Document consumer feedback and appeal mechanisms
• ☐ Store assessments securely (3-year retention minimum)
• ☐ Update assessments when AI tools change significantly
• ☐ Maintain version control for assessment updates

Step 2.3: California Risk Assessments (CPRA ADMT)

Requirement: Risk assessment for automated decision-making technology (ADMT) under California CPRA.

When ADMT Applies:

• Employer processes information of 100,000+ California residents annually
• Uses automated systems to make employment decisions
• Technology analyzes personal information to make or substantially assist decisions

Risk Assessment Elements:

• Data types processed: What personal information AI analyzes
• Sensitive data: Whether AI uses sensitive personal information (race, religion, health, etc.)
• Decision impact: Consequences of AI decisions on individuals
• Discrimination risk: Potential for disparate impact on protected groups
• Privacy risks: Data security and unauthorized access risks
• Safeguards: Technical and organizational measures to mitigate risks
• Human review: Level of human involvement in final decisions

Actionable Checklist:

• ☐ Determine if CPRA ADMT requirements apply (100K+ CA residents threshold)
• ☐ Create California-specific risk assessment template
• ☐ Complete risk assessment for each ADMT
• ☐ Document safeguards addressing identified risks
• ☐ Describe opt-out process and alternative evaluation methods
• ☐ Maintain assessment documentation (CPRA recordkeeping rules)
• ☐ Update assessments annually or when ADMT changes

Step 2.4: Illinois & Texas Compliance Documentation

While Illinois HB 3773 and Texas TRAIGA don't mandate bias audits, documenting voluntary testing demonstrates good faith and mitigates discrimination risk.

Recommended Documentation:

• Adverse impact analysis: Calculate selection rates by protected class (if data available)
• Vendor due diligence: Request and review vendor fairness testing
• Job-relatedness: Document how AI criteria relate to job performance
• Validation studies: Obtain or conduct validation (content, criterion, or construct validity)
• Alternative analysis: Evaluate less discriminatory alternatives if disparate impact found

Actionable Checklist:

• ☐ Request fairness/bias testing documentation from vendors
• ☐ If available, analyze selection rates by race, sex, age
• ☐ Calculate impact ratios using four-fifths rule (80% threshold)
• ☐ Document findings and any remediation steps
• ☐ Maintain testing documentation (3+ year retention)
• ☐ Repeat analysis annually or when tools change

Phase 3: Disclosure Notices & Rights (Weeks 3-5)

Step 3.1: Create Compliant Notice Templates

Each jurisdiction has specific disclosure requirements. Create templates that satisfy multiple regulations where possible, with jurisdiction-specific addenda where needed.

Universal AI Disclosure Notice (Baseline)

Use for all candidates, then add jurisdiction-specific elements:

• Clear statement: "We use artificial intelligence in our hiring process"
• Tools used: Name specific AI tools or types (resume screening, video analysis)
• Purpose: Why AI is used (efficiency, standardization, objective evaluation)
• Data analyzed: What candidate information AI reviews
• Output type: Scores, rankings, recommendations (not final decision)
• Human review: Emphasize human oversight in final decisions
• Contact: How to ask questions or raise concerns

NYC-Specific Addendum (Local Law 144):

• Timing: Provide at least 10 business days before AEDT use
• Job title and qualifications: For the position AI will evaluate
• Data retention: How long candidate data is kept
• Alternative process: Statement of alternative selection process availability (if any)
• Accommodation: How to request reasonable accommodation

Illinois-Specific Addendum (HB 3773):

• AI definition: Explain that AI includes machine learning and predictive systems
• Non-discrimination: Statement of commitment to non-discriminatory AI use
• Rights: Right to request information about AI use
• Zip code notice: If AI uses location data, clarify it's not used as a discriminatory proxy

Texas-Specific Notice (TRAIGA & CUBI):

• Intent statement: Affirmation that AI is not designed or used with discriminatory intent
• Biometric consent: Separate consent form if using facial recognition or voice analysis (CUBI)
• Biometric retention: Specify how long biometric data is kept and how it's deleted
• Security: Statement of reasonable care in protecting biometric data

Colorado-Specific Addendum (SB 205):

• Impact assessment reference: Mention that impact assessment has been conducted
• Appeal rights: Explain right to appeal AI-influenced decisions
• Data correction: Right to correct inaccurate data used by AI
• Opt-out: Availability of alternative evaluation (if offered)

California-Specific Addendum (CPRA ADMT):

• ADMT disclosure: Statement that automated decision-making is used
• Opt-out right: Clear explanation of how to opt out and request human review
• Personal information: Types of data processed by ADMT
• Response timeline: Employer will respond to opt-out requests within 45 days

Actionable Checklist:

• ☐ Draft universal AI disclosure notice (baseline)
• ☐ Create jurisdiction-specific addenda (NYC, IL, TX, CO, CA)
• ☐ Draft biometric consent form (Texas CUBI, if applicable)
• ☐ Review all notices with legal counsel
• ☐ Test notices for plain language readability (8th grade level recommended)
• ☐ Ensure accessibility (screen reader compatible, available in alternate formats)
• ☐ Translate notices for non-English speakers (Spanish minimum for TX, CA)
• ☐ Finalize and approve all templates

Step 3.2: Integrate Notices into Hiring Workflow

Notice delivery must be timely, trackable, and verifiable. Integrate into your ATS or hiring process at the right touchpoints.

Delivery Touchpoints:

• Job postings: Include general AI use statement or link to full notice
• Application submission: Display notice upon application completion
• Pre-assessment: Show specific notice before AI-evaluated test or video interview
• Email confirmations: Include notice in application received/interview scheduled emails
• Careers page: General AI hiring practices page linked from navigation

Timing Compliance:

• NYC (Local Law 144): At least 10 business days before AEDT use
• Illinois (HB 3773): Before or at the time AI is first used
• Texas (TRAIGA): Before biometric data collection (CUBI); general AI disclosure recommended
• Colorado (SB 205): Before AI system influences decisions
• California (CPRA): Before or at the time of data collection

Actionable Checklist:

• ☐ Update job posting templates with AI disclosure language
• ☐ Configure ATS to display notice at application completion
• ☐ Add notice display before AI assessments (video interview, skills test)
• ☐ Update application confirmation email templates
• ☐ Create AI hiring practices page on careers site
• ☐ Implement notice delivery tracking (timestamp, candidate ID, notice version)
• ☐ Test end-to-end candidate journey for all notice touchpoints
• ☐ Verify timing compliance for each jurisdiction
• ☐ Create notice delivery audit trail (for compliance verification)

Step 3.3: Establish Candidate Rights Processes

Opt-Out Process (Colorado, California)

Requirements:

• Clear instructions in disclosure notice
• Designated email or form for opt-out requests
• Alternative evaluation process (non-AI review)
• Response within required timeline (CA: 45 days)
• No adverse treatment for opting out

Implementation Steps:

• ☐ Create opt-out request form or designated email
• ☐ Define alternative evaluation workflow (human-only review)
• ☐ Train recruiters on processing opt-out requests
• ☐ Establish intake tracking system
• ☐ Set up automated response confirmation
• ☐ Create manual review queue for opt-out candidates
• ☐ Document opt-out processing procedures
• ☐ Monitor response times (target: within 5 business days for acknowledgment)

Appeal Process (Colorado)

Requirements:

• Right to appeal AI-influenced adverse decisions
• Human review of AI output and decision
• Opportunity to correct inaccurate data
• Reasonable timeframe for appeal resolution
• Written explanation of appeal outcome

Implementation Steps:

• ☐ Create appeal submission process (form or email)
• ☐ Designate appeal reviewers (senior HR or hiring managers)
• ☐ Define appeal review procedures (review AI output, reassess qualifications)
• ☐ Establish resolution timeline (recommend 15 business days)
• ☐ Create appeal response templates
• ☐ Document appeal outcomes and decision rationale
• ☐ Train staff on appeal procedures

Access Requests (All Jurisdictions)

Requirements:

• Provide information about AI use upon request
• Explain what data was analyzed and how
• Clarify how AI influenced the decision
• Identity verification before disclosing personal data

Implementation Steps:

• ☐ Create access request intake process
• ☐ Develop response templates explaining AI use
• ☐ Define identity verification procedures
• ☐ Train staff on responding to access requests
• ☐ Establish response timeline (recommend 30 days)
• ☐ Document request handling procedures

Phase 4: Training & Change Management (Weeks 4-6)

Step 4.1: Develop Training Program

Training Curriculum Topics:

• Regulatory overview: NYC, IL, TX, CO, CA requirements and timelines
• Tool identification: Which AI systems trigger compliance obligations
• Notice delivery: When and how to provide disclosures
• Biometric consent: (TX) When and how to obtain CUBI consent
• Candidate rights: Processing opt-out, appeal, and access requests
• Bias awareness: Recognizing and reporting potential discriminatory outcomes
• Documentation: What records to keep and for how long
• Escalation: When to involve legal/compliance teams
• Vendor coordination: How to work with AI vendors on compliance

Training Formats:

• Live sessions: Interactive training for HR and recruiting teams (2 hours)
• E-learning modules: Self-paced online training (60-90 minutes)
• Quick reference guides: One-page cheat sheets for common scenarios
• Video tutorials: 5-10 minute clips on specific tasks
• FAQs: Searchable knowledge base

Actionable Checklist:

• ☐ Create training curriculum outline
• ☐ Develop training materials (slides, videos, guides)
• ☐ Create knowledge assessment/quiz (10-15 questions)
• ☐ Build quick reference guides for each role
• ☐ Set up e-learning platform or LMS
• ☐ Schedule live training sessions
• ☐ Finalize training content and materials

Step 4.2: Deliver Training

Roles to Train:

• Recruiters: Front-line staff handling candidates daily
• HR generalists: Support staff involved in hiring
• Hiring managers: Decision-makers using AI outputs
• Talent acquisition leaders: Oversight and escalation
• Compliance/legal teams: Deep-dive training on all regulations
• IT/HR systems admins: Technical implementation and troubleshooting
• Third-party recruiters: External partners using your AI tools

Actionable Checklist:

• ☐ Schedule training sessions (in-person or virtual)
• ☐ Assign e-learning modules to staff
• ☐ Conduct live training sessions
• ☐ Administer knowledge assessments
• ☐ Provide remedial training for those who don't pass
• ☐ Collect signed training acknowledgments
• ☐ Document training completion dates
• ☐ Distribute quick reference guides
• ☐ Set up ongoing Q&A support channel (Slack, Teams, email)

Phase 5: Vendor Management (Weeks 3-7)

Step 5.1: Vendor Compliance Assessment

For Each AI Vendor, Request:

• Bias audit reports: (NYC) Independent audit documentation
• Fairness testing: Internal or third-party testing results
• Disclosure-ready explanations: Plain language description of how AI works
• Opt-out capabilities: Technical ability to exclude candidates from AI
• Data handling: How candidate data is stored, transmitted, and deleted
• Biometric compliance: (TX) CUBI-compliant consent and data handling
• Algorithm transparency: High-level explanation of AI methodology
• Change notifications: How vendor communicates algorithm updates
• Indemnification: Vendor's willingness to share compliance liability

Actionable Checklist:

• ☐ Create vendor compliance questionnaire
• ☐ Send questionnaire to all AI vendors
• ☐ Schedule vendor compliance review meetings
• ☐ Review vendor-provided documentation
• ☐ Identify gaps in vendor compliance support
• ☐ Request missing documentation or capabilities
• ☐ Evaluate whether to continue using vendors with significant gaps
• ☐ Document vendor assessment results

Step 5.2: Update Vendor Contracts

Contract Provisions to Add:

• Compliance obligations: Vendor represents tools comply with applicable AI laws
• Documentation delivery: Vendor must provide bias audits, fairness testing upon request
• Audit support: Vendor will cooperate with employer's compliance audits
• Change notifications: Vendor must notify of algorithm changes that could affect fairness
• Indemnification: Vendor indemnifies employer for vendor-caused compliance failures
• Termination: Right to terminate if vendor doesn't support compliance
• Data deletion: Vendor must delete candidate data upon request or retention period expiration

Actionable Checklist:

• ☐ Review all AI vendor contracts
• ☐ Draft compliance addendum or contract amendment
• ☐ Negotiate with vendors to accept compliance terms
• ☐ Obtain executed amendments or new contracts
• ☐ Document vendor refusals (evaluate continuing relationship)
• ☐ Store updated contracts centrally

Phase 6: Go-Live & Monitoring (Week 8+)

Step 6.1: Pre-Launch Verification

Final Checks Before Effective Date:

• ☐ All disclosure notices deployed and tested
• ☐ Notice delivery tracking operational
• ☐ Bias audits current and publicly posted (NYC)
• ☐ Impact assessments completed and stored (CO)
• ☐ Risk assessments documented (CA)
• ☐ Biometric consent process operational (TX)
• ☐ Training completed for all staff
• ☐ Opt-out process tested end-to-end
• ☐ Appeal process tested
• ☐ Access request intake operational
• ☐ Vendor contracts updated
• ☐ Compliance documentation organized and accessible
• ☐ Backup plan in place for system failures

Step 6.2: Launch Monitoring (First 30 Days)

Daily Checks (Week 1):

• ☐ Verify notice delivery to all candidates
• ☐ Review notice delivery logs for failures
• ☐ Check for incoming opt-out, appeal, or access requests
• ☐ Monitor candidate questions or confusion
• ☐ Log and escalate any compliance issues immediately
• ☐ Daily stand-up with compliance team

Weekly Checks (Weeks 2-4):

• ☐ Review notice delivery metrics
• ☐ Process pending candidate requests
• ☐ Analyze common questions or friction points
• ☐ Refine processes based on early learnings
• ☐ Update training materials if gaps identified
• ☐ Weekly compliance team meeting

Ongoing Compliance (Post-Launch)

Monthly Activities:

• ☐ Review notice delivery metrics (target: 100% coverage)
• ☐ Process opt-out, appeal, and access requests
• ☐ Monitor regulatory agency guidance and enforcement actions
• ☐ Review compliance incident log
• ☐ Update processes based on new guidance
• ☐ Monthly compliance dashboard to leadership

Quarterly Activities:

• ☐ Calculate selection rates by protected class (if data available)
• ☐ Analyze adverse impact ratios
• ☐ Review AI tool effectiveness and fairness
• ☐ Update documentation (policies, procedures, assessments)
• ☐ Vendor performance review
• ☐ Compliance gap analysis (identify emerging issues)
• ☐ Quarterly leadership report

Annual Activities:

• ☐ Renew bias audits for all AEDTs (NYC)
• ☐ Update impact assessments (CO)
• ☐ Refresh risk assessments (CA)
• ☐ Complete annual compliance training refresh
• ☐ Full policy and procedure review
• ☐ Comprehensive vendor reassessment
• ☐ AI tool inventory update
• ☐ Compliance audit (internal or external)
• ☐ Annual board/leadership report

Quick Reference: Requirements by Jurisdiction

Frequently Asked Questions

How long does full implementation take?

Minimum 8-12 weeks if starting from scratch. Bias audits (NYC) alone can take 4-6 weeks. Vendor coordination, training development, and system integration add time. Start immediately to meet January/February 2026 effective dates.

What if we can't get bias audits from our vendors in time?

Commission independent audits from third-party firms. Several audit providers serve multiple employers using the same tools. Alternatively, discontinue using unaudited tools for NYC positions until audits are available.

Do we need separate processes for each state, or can we use one universal approach?

Recommended: Universal baseline + state-specific addenda. Create compliant notices and processes that satisfy the most stringent requirements (NYC, Colorado), then add state-specific elements where needed. This approach is simpler to manage than maintaining separate processes.

What happens if we miss a compliance deadline?

Penalties vary by jurisdiction:

• NYC: $500 first violation, up to $1,500 per subsequent violation
• Illinois: Civil rights violations under IHRA (compensatory + punitive damages)
• Texas: AG enforcement (penalties TBD), plus Chapter 21/CUBI liability
• Colorado: AG enforcement beginning 2026, private action from 2029
• California: CPRA penalties up to $7,500 per intentional violation

Beyond penalties, non-compliance creates discrimination liability, reputational risk, and candidate trust issues. Prioritize compliance to avoid enforcement actions.

Can we continue using AI tools while working toward compliance?

Depends on the tool and jurisdiction:

• NYC: Cannot use AEDTs without current bias audit and proper notice
• Other jurisdictions: Can continue with proper disclosure while completing assessments
• High-risk tools: Consider pausing tools with known bias until compliant

Document good-faith compliance efforts to demonstrate intent to comply if questioned.

Who should own AI hiring compliance in our organization?

Cross-functional ownership works best:

• HR/Talent Acquisition: Day-to-day implementation and training
• Legal/Compliance: Regulatory interpretation and risk management
• IT/Security: Technical implementation and data protection
• Executive sponsor: CHRO or VP HR for leadership support and budget

Designate a compliance project manager to coordinate across teams and track progress.

Related Resources

• NYC Local Law 144 Complete Guide
• Illinois HB 3773 Compliance Guide
• Texas TRAIGA Compliance Guide
• Colorado SB 205 for Employers
• AI Disclosure Notice Templates
• Building an AI Compliance Program
• Training HR Teams on AI Compliance
• AI Vendor Assessment Guide
• AI Bias Audit Guide
• Free Compliance Scorecard (Assess Your Risk)

How EmployArmor Automates This Entire Checklist

EmployArmor's compliance platform handles every step of this checklist automatically:

• AI inventory: Auto-discover AI tools across your HR tech stack
• Multi-state compliance: Generate jurisdiction-specific notices and track delivery
• Bias audits: Connect with audit providers and track renewal deadlines
• Assessments: Guided workflows for impact/risk assessments (CO, CA)
• Candidate rights: Automated opt-out, appeal, and access request processing
• Training: Built-in training modules with completion tracking
• Vendor management: Centralized vendor documentation and contract tracking
• Ongoing monitoring: Automated compliance checks and alerts
• Reporting: Dashboard and reports for leadership