{ "file_id": "11_vertical_hr_workforce_management", "version": "2026.03.16", "schema_version": "2.2", "status": "Production Authority", "last_authoritative_sync": "2026-03-16", "description": "Comprehensive enumeration library for the Human Resources & Workforce Management vertical. Covers every subdomain where agentic AI is actively deployed as of March 2026: AI-assisted recruitment and applicant tracking (EU AI Act Annex III para 4, EEOC, NYC Local Law 144), automated employment decision tools and bias audit compliance, performance management and employee monitoring AI, compensation equity analysis, workforce planning and people analytics, learning and development AI, employee relations and HR case management, DEI programme measurement, payroll and benefits administration, and occupational health and safety AI. Designed for use as OTel span attributes in an agentic AI SDK and as policy vocabulary in an OPA Rego GRC portal.", "vertical_metadata": { "vertical_key": "hr_workforce", "industry": "Human Resources & Workforce Management", "primary_standards": [ "EU AI Act (2024/1689) Annex III para 4 — High-risk AI in employment, worker management, and access to self-employment", "EU AI Act Article 5(1)(b) — Prohibition on AI exploiting vulnerabilities of workers", "EU AI Act Article 10 — Training data governance and bias mitigation for high-risk AI", "EEOC Uniform Guidelines on Employee Selection Procedures (29 CFR Part 1607) — Adverse impact / four-fifths rule", "EEOC Technical Assistance on AI and Algorithmic Fairness (2023)", "EEOC Strategic Enforcement Plan FY2024-2028 — AI-enabled discrimination as enforcement priority", "NYC Local Law 144 (2021) — Automated Employment Decision Tools (AEDT) — effective July 5, 2023", "NYC Local Law 144 Rules (DCWP) — Bias audit and notice requirements", "Illinois Artificial Intelligence Video Interview Act (AIVIA 2020) — AI video interview consent and bias testing", "Colorado AI Act (SB 24-205, 2024) — High-risk AI systems including employment decisions; effective February 2026", "California AB 2930 (2024) — Automated decision system transparency; effective January 2026", "EU GDPR Article 22 — Automated individual decision-making including HR profiling", "EU General Data Protection Regulation — Employment data processing (Recital 155; Article 88)", "EU Directive 2019/1152 — Transparent and Predictable Working Conditions", "EU Directive 2023/970 — Pay Transparency Directive (mandatory from June 2026)", "EU Directive 2002/14/EC — Information and Consultation of Employees (works council AI consultation)", "Title VII of the Civil Rights Act (42 USC § 2000e) — Prohibited employment discrimination", "Age Discrimination in Employment Act (ADEA 29 USC § 621) — Age discrimination in hiring and employment", "Americans with Disabilities Act (ADA Title I) — Employment discrimination; AI assessment accommodations", "Equal Pay Act (29 USC § 206(d)) — Compensation equity", "NLRA (29 USC § 151) — National Labour Relations Act; employee monitoring and AI surveillance", "WARN Act (29 USC § 2101) — Worker Adjustment and Retraining Notification; AI-driven layoff decisions", "FLSA (29 USC § 201) — Fair Labour Standards Act; AI-driven scheduling and wage calculation", "OSHA 29 CFR Part 1904 — Recordkeeping and reporting occupational injuries and illnesses", "ISO 30414:2018 — Human capital reporting guidelines", "Society for Industrial and Organizational Psychology (SIOP) Principles for the Validation and Use of Personnel Selection Procedures (2018)" ], "primary_source_urls": [ "https://www.eeoc.gov/ai-and-algorithmic-fairness", "https://rules.cityofnewyork.us/rule/automated-employment-decision-tools/", "https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A32024R1689", "https://gdpr-info.eu/art-22-gdpr/", "https://www.dol.gov/agencies/whd/flsa", "https://www.osha.gov/recordkeeping", "https://www.iso.org/standard/69030.html" ], "otel_namespace": "hr", "opa_namespace": "data.hr", "agentic_ai_deployment_context": "As of March 2026, agentic AI in HR and workforce management is deployed across: AI resume screening and candidate ranking (most common HR AI deployment; subject to NYC LL144, EEOC, EU AI Act Annex III), AI video interview analysis (facial expression, tone, language; subject to Illinois AIVIA), AI-driven performance evaluation and continuous feedback generation, predictive attrition and flight risk scoring, compensation benchmarking and pay equity analysis (mandated reporting under EU Pay Transparency Directive from June 2026), workforce planning and headcount forecasting, learning recommendation engines and skill gap analysis, employee sentiment and engagement analysis (may constitute monitoring under NLRA), AI scheduling and shift optimisation (FLSA compliance), AI-powered HR service desk and case triage, DEI analytics and representation gap identification, and AI-assisted occupational health and safety monitoring. The EU AI Act Annex III para 4 explicitly classifies AI used in recruitment, selection, task allocation, performance monitoring, promotion, termination, and access to self-employment as high-risk — applying from August 2, 2026 with the full conformity assessment, logging, human oversight, and transparency obligations of Title III Chapter 2. NYC Local Law 144 is the most operationally specific binding regulation globally — it requires annual independent bias audits, public posting of audit results, and candidate/employee notice before any AEDT is used.", "key_regulatory_risk_note": "NYC Local Law 144 (effective July 5, 2023) requires employers and employment agencies using Automated Employment Decision Tools (AEDTs) in NYC to: conduct or commission an independent bias audit within one year prior to use; publish a summary of the audit results on their website; provide candidates and employees with notice at least 10 business days before the AEDT is used; and offer an alternative selection process on request. The Colorado AI Act (SB 24-205, effective February 1, 2026) extends similar obligations statewide for high-risk AI in consequential decisions including employment. The EU Pay Transparency Directive (2023/970) requires member state transposition by June 2026 — employers with 100+ employees must conduct joint pay assessments with works councils where gender pay gaps exceed 5% and cannot be justified. EU AI Act Annex III para 4 makes recruitment, selection, task allocation, and performance monitoring AI high-risk — the August 2026 application date is approaching. Works councils in EU member states with AI consultation rights (DE, FR, NL, AT, BE) must be informed and consulted before deploying new HR AI systems." }, "subdomains": [ { "subdomain": "Recruitment & Applicant Tracking", "description": "Covers EU AI Act Annex III para 4, EEOC, and NYC Local Law 144 enumerations for AI-assisted recruitment lifecycle management, candidate screening, and automated employment decision tools. AI recruitment systems are the most regulated HR AI category globally as of 2026.", "relevant_standards": [ "EU AI Act (2024/1689) Annex III para 4(a) — AI in recruitment and selection is high-risk", "NYC Local Law 144 (2021) — AEDT bias audit and notice requirements", "EEOC Uniform Guidelines on Employee Selection Procedures (29 CFR Part 1607)", "EEOC Technical Assistance on AI and Algorithmic Fairness (2023)", "SIOP Principles for the Validation and Use of Personnel Selection Procedures (2018)", "Colorado AI Act (SB 24-205) — Effective February 2026", "California AB 2930 — Effective January 2026" ], "categories": [ { "enum_name": "RecruitmentStage", "label": "Recruitment Stage", "otel_attribute": "hr.recruitment.stage", "opa_policy_path": "data.hr.recruitment.stage", "rego_input_key": "hr_recruitment_stage", "stability": "stable", "description": "Recruitment lifecycle stage for a candidate application. AI recruitment agents tag every action (screening, ranking, scheduling, assessment) with the stage at which the action occurs. Stage determines which bias audit obligations and candidate notice requirements apply under NYC LL144 and EU AI Act Annex III para 4.", "permitted_values": [ "job_posted", "application_received", "resume_screening", "ai_assessment", "phone_screen", "ai_video_interview_analysis", "interview_scheduled", "interview_completed", "reference_check", "background_check", "offer_extended", "offer_accepted", "offer_declined", "hired", "rejected", "withdrawn", "pipeline_hold" ], "value_labels": { "job_posted": "Job Posted", "application_received": "Application Received", "resume_screening": "Resume Screening", "ai_assessment": "AI Assessment", "phone_screen": "Phone Screen", "ai_video_interview_analysis": "AI Video Interview Analysis", "interview_scheduled": "Interview Scheduled", "interview_completed": "Interview Completed", "reference_check": "Reference Check", "background_check": "Background Check", "offer_extended": "Offer Extended", "offer_accepted": "Offer Accepted", "offer_declined": "Offer Declined", "hired": "Hired", "rejected": "Rejected", "withdrawn": "Withdrawn", "pipeline_hold": "Pipeline Hold" }, "code_definitions": { "resume_screening": "AI reviewing and scoring resumes against job requirements; AEDT if output substantially assists employment decision per NYC LL144 definition", "ai_assessment": "AI-administered cognitive, personality, or skills assessment; subject to ADA accommodation requirements and EEOC Uniform Guidelines validity evidence", "ai_video_interview_analysis": "AI analysing video interview recordings for speech patterns, facial expressions, or content scoring; subject to Illinois AIVIA consent requirements and EU AI Act biometrics restrictions", "pipeline_hold": "Candidate is qualified but no current opening; AI-managed talent pool for future consideration" }, "regulatory_mappings": { "eu_ai_act_annex3_4a": "EU AI Act Annex III para 4(a) — AI in recruitment and selection is high-risk from August 2, 2026; conformity assessment, logging, transparency, and human oversight required", "nyc_ll144": "NYC Local Law 144 — AEDT use at 'resume_screening', 'ai_assessment', and 'ai_video_interview_analysis' stages requires prior bias audit and candidate notice at least 10 business days before use", "eeoc_title_vii": "Title VII / EEOC Uniform Guidelines — Selection procedures at all stages must be validated and not produce unlawful adverse impact against protected classes", "illinois_aivia": "Illinois AIVIA — 'ai_video_interview_analysis' stage requires advance notice to candidates and employer consent; annual bias testing required" }, "use_case": "AI recruitment agent tags every candidate touchpoint with recruitment stage. OPA policy enforces that 'resume_screening' and 'ai_assessment' stages require bias audit on file and candidate notice delivered before the AI output influences the selection decision.", "source": "EU AI Act Annex III para 4; NYC Local Law 144 DCWP Rules; EEOC Uniform Guidelines 29 CFR Part 1607", "source_url": "https://www.eeoc.gov/ai-and-algorithmic-fairness" }, { "enum_name": "AIEmploymentDecisionType", "label": "AI Employment Decision Type", "otel_attribute": "hr.ai_decision.type", "opa_policy_path": "data.hr.ai_decision.type", "rego_input_key": "hr_ai_decision_type", "stability": "stable", "description": "Type of employment-related decision materially assisted or made by an AI system. Every type in this list is high-risk AI under EU AI Act Annex III para 4. Each type also requires evaluation against EEOC Uniform Guidelines for adverse impact, and triggers NYC LL144 AEDT obligations where applicable to NYC-based employment.", "permitted_values": [ "resume_ranking", "candidate_screening_pass_fail", "interview_scheduling_prioritisation", "ai_video_interview_scoring", "skills_assessment_scoring", "performance_evaluation_ai_generated", "performance_rating_calibration", "promotion_recommendation", "succession_planning_identification", "compensation_adjustment_recommendation", "pay_equity_gap_identification", "termination_risk_flag", "involuntary_separation_recommendation", "workforce_reduction_targeting", "task_allocation_assignment", "shift_scheduling_optimisation", "learning_path_recommendation", "attrition_flight_risk_score" ], "value_labels": { "resume_ranking": "Resume Ranking", "candidate_screening_pass_fail": "Candidate Screening Pass Fail", "interview_scheduling_prioritisation": "Interview Scheduling Prioritisation", "ai_video_interview_scoring": "AI Video Interview Scoring", "skills_assessment_scoring": "Skills Assessment Scoring", "performance_evaluation_ai_generated": "Performance Evaluation AI Generated", "performance_rating_calibration": "Performance Rating Calibration", "promotion_recommendation": "Promotion Recommendation", "succession_planning_identification": "Succession Planning Identification", "compensation_adjustment_recommendation": "Compensation Adjustment Recommendation", "pay_equity_gap_identification": "Pay Equity Gap Identification", "termination_risk_flag": "Termination Risk Flag", "involuntary_separation_recommendation": "Involuntary Separation Recommendation", "workforce_reduction_targeting": "Workforce Reduction Targeting", "task_allocation_assignment": "AI Dynamically Allocating Tasks, Shifts, or Work Assignments to Workers; EU AI Act Annex Iii Para 4(a)(ii)", "shift_scheduling_optimisation": "Shift Scheduling Optimisation", "learning_path_recommendation": "Learning Path Recommendation", "attrition_flight_risk_score": "Attrition Flight Risk Score" }, "code_definitions": { "candidate_screening_pass_fail": "AI producing a binary pass/fail or tiered ranking substantially assisting a decision to proceed or not proceed with a candidate; core NYC LL144 AEDT definition", "termination_risk_flag": "AI flagging employees as at risk of performance-based or conduct-based termination; high-risk under EU AI Act Annex III para 4(a)(iv)", "workforce_reduction_targeting": "AI identifying employees for inclusion in a reduction-in-force; WARN Act assessment required for large-scale AI-assisted layoffs", "task_allocation_assignment": "AI dynamically allocating tasks, shifts, or work assignments to workers; EU AI Act Annex III para 4(a)(ii) — monitoring, task allocation, and evaluation during work relationship" }, "regulatory_mappings": { "eu_ai_act_annex3_4a": "EU AI Act Annex III para 4(a)(i-iv) — All decision types listed are high-risk AI; full Title III Chapter 2 obligations apply", "nyc_ll144": "NYC LL144 — 'resume_ranking', 'candidate_screening_pass_fail', 'ai_video_interview_scoring', 'skills_assessment_scoring' are AEDTs requiring annual bias audit", "eeoc_adea": "ADEA — 'attrition_flight_risk_score', 'workforce_reduction_targeting', and 'termination_risk_flag' must not produce age-based disparate impact on workers 40+", "warn_act": "WARN Act — 'workforce_reduction_targeting' triggering 50+ separations at a single site within 30 days may require 60-day advance notice", "eu_pay_transparency": "EU Pay Transparency Directive — 'compensation_adjustment_recommendation' and 'pay_equity_gap_identification' must support gender pay gap reporting from June 2026" }, "use_case": "Every AI HR agent action is tagged with its decision type. OPA policy enforces that 'termination_risk_flag', 'involuntary_separation_recommendation', and 'workforce_reduction_targeting' outputs require HR Business Partner and Legal review before any communication to managers or employees.", "source": "EU AI Act Annex III para 4; NYC Local Law 144 DCWP Rules; EEOC Strategic Enforcement Plan FY2024-2028", "source_url": "https://rules.cityofnewyork.us/rule/automated-employment-decision-tools/" }, { "enum_name": "CandidateNoticeStatus", "label": "Candidate Notice Status", "otel_attribute": "hr.recruitment.candidate_notice_status", "opa_policy_path": "data.hr.recruitment.candidate_notice_status", "rego_input_key": "hr_recruitment_candidate_notice_status", "stability": "stable", "description": "Status of required candidate notice that an AEDT is being used, as required by NYC Local Law 144 and emerging state AI laws. AI recruitment agents must log notice delivery and confirm the required notice window has elapsed before using AEDT output to influence a hiring decision.", "permitted_values": [ "notice_not_required", "notice_pending_delivery", "notice_delivered_window_running", "notice_window_elapsed_aedt_permitted", "alternative_process_requested", "alternative_process_in_progress", "notice_failed_delivery" ], "value_labels": { "notice_not_required": "Notice not Required", "notice_pending_delivery": "Notice Pending Delivery", "notice_delivered_window_running": "Notice Delivered Window Running", "notice_window_elapsed_aedt_permitted": "Notice Window Elapsed Aedt Permitted", "alternative_process_requested": "Alternative Process Requested", "alternative_process_in_progress": "Alternative Process in Progress", "notice_failed_delivery": "Notice Failed Delivery" }, "code_definitions": { "notice_delivered_window_running": "NYC LL144 requires at least 10 business days notice before AEDT is used; notice delivered but window has not yet elapsed; AEDT output cannot yet influence decision", "notice_window_elapsed_aedt_permitted": "10 business day window has elapsed; AEDT output may now be used in the selection process", "alternative_process_requested": "Candidate has requested an alternative selection process per NYC LL144; AEDT must not be used for this candidate until alternative process is completed or declined" }, "regulatory_mappings": { "nyc_ll144": "NYC LL144 § 20-871(b) — Employer must notify candidates at least 10 business days before AEDT use; must accommodate requests for alternative process", "colorado_ai_act": "Colorado SB 24-205 — Prior notice to individuals subject to high-risk AI decisions in employment; effective February 2026", "california_ab2930": "California AB 2930 — Notice and explanation rights for individuals subject to automated decision systems; effective January 2026" }, "use_case": "OPA policy blocks AI recruitment agent from using AEDT output to influence any hiring decision unless candidate_notice_status is 'notice_window_elapsed_aedt_permitted'. 'Alternative_process_requested' status blocks AEDT use entirely for that candidate until alternative process outcome.", "source": "NYC Local Law 144 DCWP Rules § 20-871; Colorado SB 24-205; California AB 2930", "source_url": "https://rules.cityofnewyork.us/rule/automated-employment-decision-tools/" }, { "enum_name": "RequisitionApprovalStatus", "label": "Requisition Approval Status", "otel_attribute": "hr.recruitment.requisition_status", "opa_policy_path": "data.hr.recruitment.requisition_status", "rego_input_key": "hr_recruitment_requisition_status", "stability": "proposed", "description": "Status of a job requisition in the hiring approval workflow. AI talent acquisition agents use this to gate candidate-facing actions — no outreach, screening, or assessment may occur before the requisition is approved.", "permitted_values": [ "draft", "pending_hm_approval", "pending_finance_approval", "pending_dei_review", "approved_open", "on_hold", "filled", "cancelled" ], "value_labels": { "draft": "Draft", "pending_hm_approval": "Pending HM Approval", "pending_finance_approval": "Pending Finance Approval", "pending_dei_review": "Pending DEI Review", "approved_open": "Approved Open", "on_hold": "On Hold", "filled": "Filled", "cancelled": "Cancelled" }, "use_case": "AI talent sourcing agent checks requisition status before initiating any candidate outreach. 'Pending_dei_review' status ensures new requisitions are assessed for inclusive job description language before publishing. OPA policy blocks AI from advancing candidates to interview without 'approved_open' status.", "source": "SHRM hiring process framework; ATS platform requisition workflows", "source_url": "https://www.shrm.org/" } ] }, { "subdomain": "Bias Audit & Algorithmic Fairness", "description": "Covers EEOC Uniform Guidelines, NYC Local Law 144, and EU AI Act Article 10 enumerations for AI employment bias auditing, adverse impact analysis, and remediation tracking. These enums operationalise the most specific legal requirements for HR AI compliance.", "relevant_standards": [ "EEOC Uniform Guidelines on Employee Selection Procedures (29 CFR Part 1607) — Four-fifths / 80% rule for adverse impact", "EEOC Technical Assistance on AI and Algorithmic Fairness (2023)", "NYC Local Law 144 DCWP Bias Audit Rules (effective July 5, 2023)", "EU AI Act Article 10 — Training data governance and bias mitigation for high-risk AI", "EU AI Act Article 9 — Risk management system including bias testing", "SIOP Principles — Validity evidence requirements for personnel selection", "Colorado SB 24-205 — Impact assessment for high-risk AI", "ISO/IEC TR 24027:2021 — Bias in AI systems and AI aided decision making" ], "categories": [ { "enum_name": "BiasAuditOutcome", "label": "Bias Audit Outcome", "otel_attribute": "hr.bias_audit.outcome", "opa_policy_path": "data.hr.bias_audit.outcome", "rego_input_key": "hr_bias_audit_outcome", "stability": "stable", "description": "Outcome of an independent bias audit for an Automated Employment Decision Tool (AEDT) or other high-risk HR AI system. NYC Local Law 144 requires annual independent audits. All outcome values except 'compliant_no_disparate_impact' and 'compliant_with_conditions' block operational use of the AEDT until remediation.", "permitted_values": [ "compliant_no_disparate_impact", "compliant_with_conditions", "disparate_impact_detected_below_threshold", "disparate_impact_detected_above_threshold", "requires_remediation", "remediation_in_progress", "remediation_completed_reaudit_required", "audit_failed_methodology", "audit_not_yet_conducted", "audit_expired_renewal_required" ], "value_labels": { "compliant_no_disparate_impact": "Compliant No Disparate Impact", "compliant_with_conditions": "Compliant with Conditions", "disparate_impact_detected_below_threshold": "Disparate Impact Detected Below Threshold", "disparate_impact_detected_above_threshold": "Disparate Impact Detected Above Threshold", "requires_remediation": "Requires Remediation", "remediation_in_progress": "Remediation in Progress", "remediation_completed_reaudit_required": "Remediation Completed Reaudit Required", "audit_failed_methodology": "Audit Failed Methodology", "audit_not_yet_conducted": "Audit not Yet Conducted", "audit_expired_renewal_required": "Audit Expired Renewal Required" }, "code_definitions": { "compliant_no_disparate_impact": "Audit found no selection rate disparity meeting the EEOC four-fifths (80%) rule threshold for any race, sex, or ethnicity category; AEDT may be used", "compliant_with_conditions": "Minor disparities identified below adverse impact threshold; auditor recommends monitoring conditions; AEDT may be used with enhanced monitoring", "disparate_impact_detected_above_threshold": "Selection rate ratio below 0.80 (four-fifths rule) for one or more protected groups; adverse impact confirmed; AEDT must be suspended or remediated before continued use", "audit_expired_renewal_required": "NYC LL144 requires annual audit; prior audit results are more than 12 months old; AEDT must be suspended until new audit is completed", "audit_failed_methodology": "Independent auditor rejected the audit due to insufficient data, invalid statistical methodology, or lack of cooperation from employer; AEDT blocked" }, "regulatory_mappings": { "nyc_ll144": "NYC LL144 — Annual independent bias audit required; audit summary must be posted publicly on employer website; 'disparate_impact_detected_above_threshold' requires AEDT suspension", "eeoc_uniform_guidelines": "29 CFR Part 1607 — Four-fifths rule: selection rate for any protected group below 80% of highest group rate indicates adverse impact", "eu_ai_act_art10": "EU AI Act Article 10 — Training, validation, and testing data must be examined for biases; bias detection findings must inform risk management system", "colorado_ai_act": "Colorado SB 24-205 — Deployers must conduct impact assessments and take corrective action for high-risk AI with discriminatory effects" }, "use_case": "OPA policy blocks any AI employment decision tool from being used in any hiring, promotion, or termination decision where bias_audit_outcome is not 'compliant_no_disparate_impact' or 'compliant_with_conditions'. 'Audit_expired_renewal_required' triggers the same block as an adverse finding.", "source": "NYC Local Law 144 DCWP Rules; EEOC Uniform Guidelines 29 CFR Part 1607; EU AI Act Article 10", "source_url": "https://rules.cityofnewyork.us/rule/automated-employment-decision-tools/" }, { "enum_name": "AdverseImpactProtectedClass", "label": "Adverse Impact Protected Class", "otel_attribute": "hr.bias_audit.protected_class", "opa_policy_path": "data.hr.bias_audit.protected_class", "rego_input_key": "hr_bias_audit_protected_class", "stability": "stable", "description": "Protected class category against which adverse impact analysis is required. NYC Local Law 144 mandates race/ethnicity and sex category analysis. EEOC Uniform Guidelines require analysis across all Title VII, ADEA, and ADA protected classes. EU AI Act Article 10 requires bias analysis across protected characteristics under EU anti-discrimination law.", "permitted_values": [ "race_ethnicity", "sex_gender", "age_40_plus", "disability_status", "national_origin", "religion", "pregnancy_status", "sexual_orientation", "gender_identity", "veteran_status", "genetic_information" ], "value_labels": { "race_ethnicity": "Race Ethnicity", "sex_gender": "Sex Gender", "age_40_plus": "Age 40 Plus", "disability_status": "Disability Status", "national_origin": "National Origin", "religion": "Religion", "pregnancy_status": "Pregnancy Status", "sexual_orientation": "Sexual Orientation", "gender_identity": "Gender Identity", "veteran_status": "Veteran Status", "genetic_information": "Genetic Information" }, "regulatory_mappings": { "nyc_ll144_mandatory": "NYC LL144 requires bias audit to calculate impact ratio by sex, race/ethnicity category at minimum", "title_vii": "Title VII — race_ethnicity, sex_gender, national_origin, religion protected", "adea": "ADEA — age_40_plus protected", "ada": "ADA — disability_status protected", "eu_ai_act_art10": "EU AI Act Article 10 and EU anti-discrimination directives — all categories applicable in EU deployment" }, "source": "NYC LL144 DCWP Rules; EEOC Uniform Guidelines 29 CFR Part 1607 § 4; Title VII, ADEA, ADA; EU Equal Treatment Directives", "source_url": "https://www.eeoc.gov/laws/guidance/questions-and-answers-clarify-and-provide-common-interpretation-uniform-guidelines" }, { "enum_name": "FairnessMitigationTechnique", "label": "Fairness Mitigation Technique", "otel_attribute": "hr.bias_audit.mitigation_technique", "opa_policy_path": "data.hr.bias_audit.mitigation_technique", "rego_input_key": "hr_bias_audit_mitigation_technique", "stability": "proposed", "description": "Bias mitigation technique applied to an HR AI model to reduce disparate impact. Logged for EU AI Act Article 10 training data governance evidence and audit trail. The technique used affects validity evidence requirements under EEOC Uniform Guidelines.", "permitted_values": [ "reweighting_training_data", "resampling_underrepresented_groups", "adversarial_debiasing", "calibrated_equalised_odds", "threshold_adjustment_per_group", "feature_removal_proxy_variables", "model_selection_fairness_constrained", "human_in_loop_override_sampling", "pre_employment_test_adverse_impact_redesign" ], "value_labels": { "reweighting_training_data": "Reweighting Training Data", "resampling_underrepresented_groups": "Resampling Underrepresented Groups", "adversarial_debiasing": "Adversarial Debiasing", "calibrated_equalised_odds": "Calibrated Equalised Odds", "threshold_adjustment_per_group": "Threshold Adjustment Per Group", "feature_removal_proxy_variables": "Feature Removal Proxy Variables", "model_selection_fairness_constrained": "Model Selection Fairness Constrained", "human_in_loop_override_sampling": "Human-in-the-Loop Override Sampling", "pre_employment_test_adverse_impact_redesign": "Pre-Employment Test Adverse Impact Redesign" }, "regulatory_mappings": { "eeoc_uniform_guidelines": "29 CFR Part 1607 § 3(B) — If adverse impact is found, employer must either eliminate it or validate the procedure under Uniform Guidelines validity standards", "eu_ai_act_art10": "EU AI Act Article 10(2)(f) — Training data must be examined for possible biases; mitigation technique and its effectiveness must be documented" }, "use_case": "AI model development agent records the mitigation technique applied during model development. This record feeds the EU AI Act Article 10 technical documentation and the bias audit evidence package submitted to the independent auditor.", "source": "EU AI Act Article 10; EEOC Uniform Guidelines; NIST AI RMF BIAS-P1; ISO/IEC TR 24027:2021", "source_url": "https://csrc.nist.gov/publications/detail/ai/100-1/final" } ] }, { "subdomain": "Performance Management & Employee Monitoring", "description": "Covers EU AI Act Annex III para 4, GDPR Article 88, and NLRA enumerations for AI-driven performance evaluation, continuous monitoring, and employee surveillance governance. These are the most contentious HR AI applications from a worker rights perspective.", "relevant_standards": [ "EU AI Act (2024/1689) Annex III para 4(a)(ii) — AI for monitoring, task allocation, and evaluation during work relationship is high-risk", "EU GDPR Article 88 — Processing of employee personal data; member state implementing laws", "NLRA (29 USC § 151) — Employee monitoring must not chill protected concerted activity", "EU Directive 2019/1152 — Transparent and predictable working conditions", "EU Works Council Directive 94/45/EC — Transnational information and consultation", "ETUC AI and Workers' Rights Declaration (2023)" ], "categories": [ { "enum_name": "PerformanceEvaluationSource", "label": "Performance Evaluation Source", "otel_attribute": "hr.performance.evaluation_source", "opa_policy_path": "data.hr.performance.evaluation_source", "rego_input_key": "hr_performance_evaluation_source", "stability": "proposed", "description": "Source of data used by an AI performance evaluation agent. EU AI Act Annex III para 4(a)(ii) makes AI monitoring and evaluation of workers high-risk. Employees must be informed of the data sources used to evaluate them under GDPR Article 13/14 and EU Directive 2019/1152.", "permitted_values": [ "manager_review_structured", "peer_360_feedback", "self_assessment", "objective_kpi_system_data", "ai_productivity_monitoring", "communication_sentiment_analysis", "code_commit_activity", "customer_satisfaction_csat", "attendance_punctuality_data", "learning_completion_data", "sales_crm_activity_data", "ticket_resolution_data" ], "value_labels": { "manager_review_structured": "Manager Review Structured", "peer_360_feedback": "Peer 360 Feedback", "self_assessment": "Self-Assessment", "objective_kpi_system_data": "Objective KPI System Data", "ai_productivity_monitoring": "AI Productivity Monitoring", "communication_sentiment_analysis": "Communication Sentiment Analysis", "code_commit_activity": "Code Commit Activity", "customer_satisfaction_csat": "Customer Satisfaction Csat", "attendance_punctuality_data": "Attendance Punctuality Data", "learning_completion_data": "Learning Completion Data", "sales_crm_activity_data": "Sales Crm Activity Data", "ticket_resolution_data": "Ticket Resolution Data" }, "regulatory_mappings": { "eu_ai_act_annex3_4a_ii": "EU AI Act Annex III para 4(a)(ii) — AI monitoring and evaluation of workers is high-risk; all data sources must be documented in technical documentation", "gdpr_art13_14": "GDPR Articles 13/14 — Employees must be informed at time of data collection of purposes and data sources used in automated performance evaluation", "nlra": "NLRA — 'communication_sentiment_analysis' monitoring must not be used to identify or suppress protected concerted activity (union organising, collective complaints)", "eu_directive_2019_1152": "EU Directive 2019/1152 Article 4 — Workers have right to information about monitoring mechanisms, including AI performance evaluation data sources" }, "use_case": "AI performance evaluation agent records all data sources used for each evaluation. This drives GDPR Article 13 privacy notice accuracy and EU AI Act technical documentation. OPA policy blocks 'communication_sentiment_analysis' source data from feeding any termination or performance improvement plan recommendation without prior works council consultation.", "source": "EU AI Act Annex III para 4(a)(ii); EU Directive 2019/1152; GDPR Article 13/14; NLRA §7 guidance", "source_url": "https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A32024R1689" }, { "enum_name": "EmployeeMonitoringType", "label": "Employee Monitoring Type", "otel_attribute": "hr.monitoring.type", "opa_policy_path": "data.hr.monitoring.type", "rego_input_key": "hr_monitoring_type", "stability": "stable", "description": "Type of AI-enabled employee monitoring. Each type carries different legal consent requirements, transparency obligations, and works council consultation requirements. Some types may be prohibited outright in certain jurisdictions.", "permitted_values": [ "productivity_keystroke_mouse_activity", "application_usage_tracking", "video_surveillance_workplace", "video_surveillance_remote_work", "email_communication_monitoring", "location_tracking_gps", "biometric_access_control", "biometric_fatigue_detection", "call_recording_analysis", "social_media_monitoring_internal", "ai_meeting_transcription_analysis", "ambient_listening_office" ], "value_labels": { "productivity_keystroke_mouse_activity": "Productivity Keystroke Mouse Activity", "application_usage_tracking": "Application Usage Tracking", "video_surveillance_workplace": "Video Surveillance Workplace", "video_surveillance_remote_work": "Video Surveillance Remote Work", "email_communication_monitoring": "Email Communication Monitoring", "location_tracking_gps": "Location Tracking Gps", "biometric_access_control": "Biometric Access Control", "biometric_fatigue_detection": "Biometric Fatigue Detection", "call_recording_analysis": "Call Recording Analysis", "social_media_monitoring_internal": "Social Media Monitoring Internal", "ai_meeting_transcription_analysis": "AI Meeting Transcription Analysis", "ambient_listening_office": "Ambient Listening Office" }, "code_definitions": { "biometric_fatigue_detection": "AI analysing facial expressions or physiological signals in real-time to detect fatigue or stress; restricted or prohibited under EU AI Act Article 5(1)(f) and GDPR special categories", "ambient_listening_office": "Always-on audio monitoring of office environments; highly contested under NLRA protected activity and EU GDPR; generally prohibited in most EU jurisdictions without explicit consent", "ai_meeting_transcription_analysis": "AI transcribing and analysing meeting content for sentiment, topic detection, or participant engagement; participant consent required under most wiretapping laws" }, "regulatory_mappings": { "eu_ai_act_art5_1f": "EU AI Act Article 5(1)(f) — Prohibition on real-time biometric categorisation for inferring sensitive attributes including emotion; 'biometric_fatigue_detection' with emotion inference is prohibited", "gdpr_art9": "GDPR Article 9 — Biometric monitoring data is special category; explicit consent or substantial public interest required", "nlra_sec7": "NLRA § 7 — Employee monitoring must not interfere with protected concerted activity; blanket communication monitoring for union-related communications is unlawful", "eu_works_councils": "EU Works Council Directive — Transnational monitoring systems must be disclosed to and negotiated with European Works Council before deployment" }, "source": "EU AI Act Article 5; GDPR Article 9; NLRA § 7; EU Works Council Directive 94/45/EC; national implementing laws", "source_url": "https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A32024R1689" }, { "enum_name": "PerformanceImprovementPlanStatus", "label": "Performance Improvement Plan Status", "otel_attribute": "hr.performance.pip_status", "opa_policy_path": "data.hr.performance.pip_status", "rego_input_key": "hr_performance_pip_status", "stability": "proposed", "description": "Status of an employee Performance Improvement Plan (PIP) workflow. AI performance management agents may identify candidates for PIP and generate draft plans but the decision to place an employee on a PIP and the plan terms must be approved by an HR Business Partner and the employee's manager.", "permitted_values": [ "not_on_pip", "ai_flagged_for_consideration", "hrbp_review_in_progress", "pip_initiated_employee_notified", "pip_active_monitoring", "pip_completed_successfully", "pip_failed_separation_process", "pip_withdrawn_performance_improved" ], "value_labels": { "not_on_pip": "Not on Pip", "ai_flagged_for_consideration": "AI Flagged for Consideration", "hrbp_review_in_progress": "Hrbp Review in Progress", "pip_initiated_employee_notified": "Pip Initiated Employee Notified", "pip_active_monitoring": "Pip Active Monitoring", "pip_completed_successfully": "Pip Completed Successfully", "pip_failed_separation_process": "Pip Failed Separation Process", "pip_withdrawn_performance_improved": "Pip Withdrawn Performance Improved" }, "regulatory_mappings": { "eu_ai_act_annex3_4a": "EU AI Act Annex III para 4(a)(iv) — AI used in termination of work relationships is high-risk; PIP leading to separation requires full HITL accountability", "gdpr_art22": "GDPR Article 22 — Employees have right not to be subject to solely automated decisions with significant effects; PIP initiation based solely on AI flagging without human review is unlawful" }, "use_case": "OPA policy enforces that no employee may be placed on a PIP solely on the basis of an AI performance flag. Status must advance to 'hrbp_review_in_progress' with documented human review before 'pip_initiated_employee_notified' is set.", "source": "EU AI Act Annex III para 4; GDPR Article 22; SHRM PIP best practices", "source_url": "https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A32024R1689" } ] }, { "subdomain": "Compensation Equity & Pay Transparency", "description": "Covers EU Pay Transparency Directive, Equal Pay Act, and AI-driven pay equity analysis enumerations. The EU Pay Transparency Directive requires member state transposition by June 2026 — employers with 100+ employees must conduct joint pay assessments where unexplained gender pay gaps exceed 5%.", "relevant_standards": [ "EU Pay Transparency Directive (2023/970) — Member state transposition by June 7, 2026", "Equal Pay Act (29 USC § 206(d)) — US federal equal pay requirements", "Lilly Ledbetter Fair Pay Act (2009) — Extended statute of limitations for pay discrimination claims", "Colorado Equal Pay for Equal Work Act (2021) — Pay range disclosure in job postings", "NYC Salary Transparency Law (Local Law 32, 2022) — Pay range disclosure", "California SB 1162 (2022) — Pay scale disclosure and pay data reporting", "Illinois Equal Pay Act (2021 amendments) — Pay data reporting" ], "categories": [ { "enum_name": "PayEquityAnalysisOutcome", "label": "Pay Equity Analysis Outcome", "otel_attribute": "hr.pay_equity.analysis_outcome", "opa_policy_path": "data.hr.pay_equity.analysis_outcome", "rego_input_key": "hr_pay_equity_analysis_outcome", "stability": "stable", "description": "Outcome of an AI-driven pay equity analysis for a cohort of employees. EU Pay Transparency Directive Article 9 requires employers with 100+ employees to conduct pay assessments and report gaps. Unexplained gender pay gaps exceeding 5% trigger mandatory joint assessment with workers' representatives.", "permitted_values": [ "equitable_no_unexplained_gap", "gap_identified_within_5pct_justified", "gap_identified_5_to_20pct_review_required", "gap_identified_above_20pct_assessment_mandatory", "gap_identified_remediation_in_progress", "gap_remediated_verified", "analysis_in_progress", "insufficient_data_for_analysis" ], "value_labels": { "equitable_no_unexplained_gap": "Equitable No Unexplained Gap", "gap_identified_within_5pct_justified": "Gap Identified Within 5pct Justified", "gap_identified_5_to_20pct_review_required": "Gap Identified 5 to 20pct Review Required", "gap_identified_above_20pct_assessment_mandatory": "Gap Identified Above 20pct Assessment Mandatory", "gap_identified_remediation_in_progress": "Gap Identified Remediation in Progress", "gap_remediated_verified": "Gap Remediated Verified", "analysis_in_progress": "Analysis in Progress", "insufficient_data_for_analysis": "Insufficient Data for Analysis" }, "code_definitions": { "gap_identified_5_to_20pct_review_required": "Gender pay gap between 5% and 20% for a comparable work group; employer must justify the gap or initiate joint pay assessment with workers' representatives", "gap_identified_above_20pct_assessment_mandatory": "Gender pay gap exceeds 20% for a comparable work group; EU Pay Transparency Directive Article 9 triggers mandatory joint pay assessment regardless of justification", "gap_identified_remediation_in_progress": "Unjustified pay gap confirmed; remediation plan agreed with workers' representatives; timeline and targets documented" }, "regulatory_mappings": { "eu_pay_transparency_directive": "EU Directive 2023/970 Article 9 — Joint pay assessment mandatory where gender pay gap exceeds 5% and cannot be justified on gender-neutral criteria; employer cannot restrict workers from accessing pay comparison data", "equal_pay_act": "EPA 29 USC § 206(d) — Pay differences for substantially equal work must be justified by seniority, merit, quantity/quality of production, or factor other than sex", "california_sb1162": "California SB 1162 — Annual pay data report to CRD; mean and median pay data by race, ethnicity, and sex for each job category" }, "use_case": "AI pay equity agent runs annual analysis across all employees grouped by comparable work. 'Gap_identified_5_to_20pct_review_required' triggers notification to CHRO and works council. OPA policy blocks AI from recommending compensation adjustments in the annual merit cycle for any group where pay equity status is 'analysis_in_progress' or 'insufficient_data_for_analysis'.", "source": "EU Pay Transparency Directive (2023/970) Articles 7–10; Equal Pay Act; California SB 1162", "source_url": "https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A32023L0970" }, { "enum_name": "CompensationBandingStatus", "label": "Compensation Banding Status", "otel_attribute": "hr.compensation.banding_status", "opa_policy_path": "data.hr.compensation.banding_status", "rego_input_key": "hr_compensation_banding_status", "stability": "proposed", "description": "Status of an employee's compensation relative to the market-benchmarked pay band for their role and level. AI compensation management agents use this to generate merit increase recommendations and flag outliers for pay equity review.", "permitted_values": [ "below_range_minimum", "in_range_low_quartile", "in_range_mid_point", "in_range_high_quartile", "above_range_maximum", "red_circle_above_range_frozen", "green_circle_below_range_priority", "band_not_established" ], "value_labels": { "below_range_minimum": "Below Range Minimum", "in_range_low_quartile": "In Range Low Quartile", "in_range_mid_point": "In Range Mid Point", "in_range_high_quartile": "In Range High Quartile", "above_range_maximum": "Above Range Maximum", "red_circle_above_range_frozen": "Red Circle Above Range Frozen", "green_circle_below_range_priority": "Green Circle Below Range Priority", "band_not_established": "Band not Established" }, "use_case": "AI compensation agent generates annual merit recommendations based on banding status and performance rating. 'Below_range_minimum' status receives priority increases. OPA policy blocks AI from recommending a zero merit increase for an employee in 'below_range_minimum' status without documented justification and manager HITL override.", "source": "WorldatWork compensation management framework; Mercer / Radford compensation benchmarking taxonomy", "source_url": "https://worldatwork.org/" } ] }, { "subdomain": "Workforce Planning & People Analytics", "description": "Covers AI-driven workforce planning, predictive attrition, and people analytics enumerations. Workforce planning AI that produces headcount reduction recommendations triggers WARN Act assessment and works council consultation obligations.", "relevant_standards": [ "WARN Act (29 USC § 2101) — 60-day notice for plant closings and mass layoffs", "EU Collective Redundancies Directive (98/59/EC) — Information and consultation obligations", "EU Works Council Directive — AI-driven workforce reduction requires EWC consultation", "ISO 30414:2018 — Human capital reporting — workforce stability metrics", "SHRM Workforce Planning Framework" ], "categories": [ { "enum_name": "AttritionRiskLevel", "label": "Attrition Risk Level", "otel_attribute": "hr.workforce.attrition_risk_level", "opa_policy_path": "data.hr.workforce.attrition_risk_level", "rego_input_key": "hr_workforce_attrition_risk_level", "stability": "proposed", "description": "AI-predicted attrition risk level for an employee. Used by HR Business Partners to prioritise retention conversations. Must not be used to pre-emptively disadvantage employees classified as high flight risk (e.g. withholding assignments, promotions, or development opportunities).", "permitted_values": [ "low_risk", "moderate_risk", "high_risk", "critical_risk_key_talent", "already_resigned", "model_confidence_insufficient" ], "value_labels": { "low_risk": "Low Risk", "moderate_risk": "Moderate Risk", "high_risk": "High Risk", "critical_risk_key_talent": "Critical Risk Key Talent", "already_resigned": "Already Resigned", "model_confidence_insufficient": "Model Confidence Insufficient" }, "regulatory_mappings": { "eu_ai_act_annex3_4a": "EU AI Act Annex III para 4(a) — AI used in decisions affecting the work relationship based on attrition risk is high-risk; cannot be used to disadvantage employees", "gdpr_art22": "GDPR Article 22 — Attrition scores must not be used as the sole basis for employment decisions with significant effects on the employee" }, "use_case": "AI workforce analytics agent generates attrition risk scores for HR HRBP dashboard. OPA policy enforces that 'high_risk' and 'critical_risk_key_talent' scores may only be used to initiate retention conversations — scores must not be used to exclude employees from promotion slates, project assignments, or merit increases.", "source": "IBM Watson Talent / Workday People Analytics attrition model taxonomy; EU AI Act Annex III para 4", "source_url": "https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A32024R1689" }, { "enum_name": "WorkforceReductionTriggerType", "label": "Workforce Reduction Trigger Type", "otel_attribute": "hr.workforce.reduction_trigger_type", "opa_policy_path": "data.hr.workforce.reduction_trigger_type", "rego_input_key": "hr_workforce_reduction_trigger_type", "stability": "stable", "description": "Type of workforce reduction event identified or recommended by an AI workforce planning agent. Each type triggers different legal notice, consultation, and WARN Act threshold assessment requirements.", "permitted_values": [ "individual_performance_separation", "role_elimination_restructure", "site_closure", "mass_layoff_us_warn_act", "collective_redundancy_eu", "voluntary_separation_programme", "contractor_workforce_reduction", "ai_automation_displacement" ], "value_labels": { "individual_performance_separation": "Individual Performance Separation", "role_elimination_restructure": "Role Elimination Restructure", "site_closure": "Site Closure", "mass_layoff_us_warn_act": "Mass Layoff Us Warn Act", "collective_redundancy_eu": "EU Collective Redundancies Directive", "voluntary_separation_programme": "Voluntary Separation Programme", "contractor_workforce_reduction": "Contractor Workforce Reduction", "ai_automation_displacement": "AI Automation Displacement" }, "code_definitions": { "mass_layoff_us_warn_act": "Reduction affecting 500+ employees, or 50-499 employees constituting 33%+ of active workforce at a single site within 30 days; triggers WARN Act 60-day notice obligation", "collective_redundancy_eu": "EU Collective Redundancies Directive — 10+ employees in establishments with 20-99 workers, or 10%+ of workforce in larger establishments; information and consultation with workers' representatives required before decisions are made", "ai_automation_displacement": "Job roles eliminated due to AI or automation introduction; EU Directive 2019/1152 Article 4 requires workers to be informed; works council consultation required in EU member states before deployment decision" }, "regulatory_mappings": { "warn_act": "WARN Act 29 USC § 2101 — 'mass_layoff_us_warn_act' and 'site_closure' types require 60-day advance written notice to affected employees, state dislocated worker units, and local government", "eu_collective_redundancy": "EU Collective Redundancies Directive 98/59/EC — 'collective_redundancy_eu' type requires information and consultation before decisions are reached; AI-generated reduction lists cannot be finalised before consultation completes", "eu_directive_2019_1152": "EU Directive 2019/1152 — 'ai_automation_displacement' requires worker notification before implementation" }, "use_case": "OPA policy blocks finalisation of any 'mass_layoff_us_warn_act' or 'collective_redundancy_eu' reduction list generated by AI workforce planning agent without documented WARN Act assessment and HR Legal sign-off. 'Ai_automation_displacement' type triggers mandatory works council consultation workflow in EU jurisdictions.", "source": "WARN Act 29 USC § 2101; EU Collective Redundancies Directive 98/59/EC; EU Directive 2019/1152", "source_url": "https://www.dol.gov/sites/dolgov/files/ETA/layoffs/pdfs/WARNAct1988.pdf" } ] }, { "subdomain": "Learning, Development & Skills Management", "description": "Covers AI-driven learning recommendation, skills taxonomy management, and career pathing enumerations. Learning AI is generally lower-risk than recruitment or termination AI but must comply with accessibility requirements and avoid reinforcing occupational segregation.", "relevant_standards": [ "SCORM 2004 / xAPI (TinCan) — Learning activity tracking standards", "IMS Global LTI Advantage — LMS integration (see also higher education vertical)", "WCAG 2.2 AA — Accessibility for AI-generated learning content", "EU AI Act Annex III para 4(a)(iii) — AI for career advancement and promotion monitoring", "Skills-based hiring frameworks — ONET, ESCO (EU), LinkedIn Skills Graph" ], "categories": [ { "enum_name": "LearningRecommendationTrigger", "label": "Learning Recommendation Trigger", "otel_attribute": "hr.learning.recommendation_trigger", "opa_policy_path": "data.hr.learning.recommendation_trigger", "rego_input_key": "hr_learning_recommendation_trigger", "stability": "proposed", "description": "The signal that triggered an AI learning recommendation for an employee. Logged for EU AI Act Annex III para 4 transparency — employees have the right to understand why they received specific development recommendations.", "permitted_values": [ "skill_gap_identified_role", "career_path_next_role", "performance_review_development_goal", "compliance_mandatory_training", "role_change_onboarding", "ai_automation_reskilling", "manager_nominated", "self_nominated", "succession_planning_readiness", "license_certification_renewal" ], "value_labels": { "skill_gap_identified_role": "Skill Gap Identified Role", "career_path_next_role": "Career Path Next Role", "performance_review_development_goal": "Performance Review Development Goal", "compliance_mandatory_training": "Compliance Mandatory Training", "role_change_onboarding": "Role Change Onboarding", "ai_automation_reskilling": "AI Automation Reskilling", "manager_nominated": "Manager Nominated", "self_nominated": "Self Nominated", "succession_planning_readiness": "Succession Planning Readiness", "license_certification_renewal": "License Certification Renewal" }, "regulatory_mappings": { "eu_ai_act_annex3_4a_iii": "EU AI Act Annex III para 4(a)(iii) — AI for career advancement monitoring and employee training access is high-risk; employees must understand basis of recommendations", "ada_title_i": "ADA Title I — Learning content and assessment tools must be accessible; 'compliance_mandatory_training' must accommodate employees with disabilities" }, "use_case": "AI learning recommendation agent surfaces recommended courses to employees. Every recommendation is tagged with its trigger. 'Ai_automation_reskilling' trigger activates enhanced career support resources and flags the employee for HR Business Partner check-in.", "source": "EU AI Act Annex III para 4; Degreed / Cornerstone learning platform recommendation taxonomy", "source_url": "https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A32024R1689" }, { "enum_name": "SkillProficiencyLevel", "label": "Skill Proficiency Level", "otel_attribute": "hr.skills.proficiency_level", "opa_policy_path": "data.hr.skills.proficiency_level", "rego_input_key": "hr_skills_proficiency_level", "stability": "stable", "description": "Employee skill proficiency level classification. AI skills assessment and talent marketplace agents use this to match employees to opportunities and identify skill gaps. Aligned to ESCO (European Skills/Competences, Qualifications and Occupations) proficiency scale.", "permitted_values": [ "awareness", "foundational", "intermediate", "advanced", "expert", "not_assessed" ], "value_labels": { "awareness": "Awareness", "foundational": "Foundational", "intermediate": "Intermediate", "advanced": "Advanced", "expert": "Expert", "not_assessed": "Not Assessed" }, "ordered": true, "value_ordinals": { "awareness": 1, "foundational": 2, "intermediate": 3, "advanced": 4, "expert": 5, "not_assessed": 6 }, "use_case": "AI skills graph agent assesses employee proficiency based on demonstrated work, completed learning, and manager endorsement. 'Not_assessed' skills are excluded from talent marketplace matching to avoid false negatives disadvantaging employees.", "source": "ESCO Skills proficiency levels; Workday Skills Cloud proficiency taxonomy; LinkedIn Skills Graph", "source_url": "https://esco.ec.europa.eu/en/about-esco/what-esco" } ] }, { "subdomain": "Employee Relations, HR Case Management & Works Councils", "description": "Covers AI-assisted HR case management, grievance tracking, and works council consultation obligation enumerations. Works council consultation rights for AI deployment vary by EU member state but are a practical constraint on HR AI deployment timelines across the EU.", "relevant_standards": [ "EU Works Council Directive 94/45/EC — Transnational information and consultation", "German Betriebsverfassungsgesetz (BetrVG) § 87(1)(6) — Works council co-determination on monitoring", "French CNIL guidance on employee monitoring", "NLRA § 8 — Unfair labour practices including unilateral implementation of AI monitoring", "ADA Title I — HR case management for disability-related accommodation requests" ], "categories": [ { "enum_name": "HRCaseType", "label": "HR Case Type", "otel_attribute": "hr.case.type", "opa_policy_path": "data.hr.case.type", "rego_input_key": "hr_case_type", "stability": "stable", "description": "Type of HR case being managed by an AI HR service agent. Case type determines routing, confidentiality requirements, regulatory timelines, and whether external reporting obligations apply.", "permitted_values": [ "harassment_complaint", "discrimination_complaint", "pay_dispute", "accommodation_request_disability", "accommodation_request_religious", "leave_of_absence_request", "grievance_general", "whistleblower_protected_disclosure", "workplace_safety_incident", "performance_dispute", "wrongful_termination_claim", "data_privacy_employee_request", "ethics_policy_violation", "retaliation_complaint" ], "value_labels": { "harassment_complaint": "Title Vii / EU Gdpr Recital 155", "discrimination_complaint": "Discrimination Complaint", "pay_dispute": "Pay Dispute", "accommodation_request_disability": "Ada Title I / EU Eeoc Equivalent", "accommodation_request_religious": "Accommodation Request Religious", "leave_of_absence_request": "Leave of Absence Request", "grievance_general": "Grievance General", "whistleblower_protected_disclosure": "EU Whistleblower Directive (2019/1937) / SEC Whistleblower Programme / Osha Whistleblower Programmes", "workplace_safety_incident": "Workplace Safety Incident", "performance_dispute": "Performance Dispute", "wrongful_termination_claim": "Wrongful Termination Claim", "data_privacy_employee_request": "Data Privacy Employee Request", "ethics_policy_violation": "Ethics Policy Violation", "retaliation_complaint": "Retaliation Complaint" }, "code_definitions": { "whistleblower_protected_disclosure": "EU Whistleblower Directive (2019/1937) / SEC whistleblower programme / OSHA whistleblower programmes — case must be isolated from all managers related to the disclosure; AI case routing must not expose the identity of the reporter", "harassment_complaint": "Title VII / EU GDPR Recital 155 — Requires confidential investigation; AI case management must maintain strict access controls; case notes are sensitive personal data", "accommodation_request_disability": "ADA Title I / EU EEOC equivalent — AI may triage but accommodation determination requires human HR and manager assessment; medical information must be kept strictly confidential" }, "regulatory_mappings": { "eu_whistleblower_directive": "EU Directive 2019/1937 — 'whistleblower_protected_disclosure' cases must have secure, confidential reporting channels; AI cannot route to implicated managers", "ada_title_i": "ADA Title I — 'accommodation_request_disability' cases require interactive process; AI cannot deny accommodation requests", "osha": "OSHA 29 CFR Part 1904 — 'workplace_safety_incident' cases require recordkeeping; OSHA reporting thresholds must be assessed" }, "use_case": "AI HR service agent classifies incoming cases. 'Whistleblower_protected_disclosure' and 'retaliation_complaint' cases are immediately routed to Legal/Compliance with strict access controls — not to the employee's management chain. OPA policy blocks AI from autonomously closing any harassment or discrimination complaint without documented HRBP investigation outcome.", "source": "EU Whistleblower Directive 2019/1937; ADA Title I; OSHA recordkeeping; Title VII", "source_url": "https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A32019L1937" }, { "enum_name": "WorksCouncilConsultationStatus", "label": "Works Council Consultation Status", "otel_attribute": "hr.works_council.consultation_status", "opa_policy_path": "data.hr.works_council.consultation_status", "rego_input_key": "hr_works_council_consultation_status", "stability": "stable", "description": "Status of works council or employee representative body consultation on the deployment of a new HR AI system. In EU member states with co-determination rights (Germany, France, Netherlands, Austria, Belgium), deployment of AI monitoring or decision-making systems without works council consultation may be invalid and constitute an unfair labour practice.", "permitted_values": [ "not_required_jurisdiction", "consultation_not_yet_initiated", "information_provided_awaiting_response", "consultation_in_progress", "consultation_completed_agreement_reached", "consultation_completed_no_agreement", "appeal_to_conciliation_board", "deployment_blocked_by_works_council" ], "value_labels": { "not_required_jurisdiction": "Not Required Jurisdiction", "consultation_not_yet_initiated": "Consultation not Yet Initiated", "information_provided_awaiting_response": "Information Provided Awaiting Response", "consultation_in_progress": "Consultation in Progress", "consultation_completed_agreement_reached": "Consultation Completed Agreement Reached", "consultation_completed_no_agreement": "Consultation Completed No Agreement", "appeal_to_conciliation_board": "Appeal to Conciliation Board", "deployment_blocked_by_works_council": "Deployment Blocked by Works Council" }, "code_definitions": { "deployment_blocked_by_works_council": "Works council has exercised co-determination right to block deployment (e.g. German BetrVG § 87 for monitoring systems); legal proceedings or conciliation required before deployment can proceed", "consultation_completed_no_agreement": "Consultation completed without agreement; deployment may still proceed in some jurisdictions after conciliation; works council may challenge in labour court" }, "regulatory_mappings": { "german_betrvg_87_1_6": "German BetrVG § 87(1)(6) — Works council has mandatory co-determination right on introduction and use of technical means to monitor employee behaviour; AI monitoring requires works agreement (Betriebsvereinbarung)", "eu_works_council_directive": "EU Works Council Directive 94/45/EC — Transnational AI deployment affecting multiple EU member state workforces requires European Works Council information and consultation", "nlra_sec8_a_5": "NLRA § 8(a)(5) — US employers with recognised unions must bargain in good faith over implementation of AI monitoring that affects terms and conditions of employment" }, "use_case": "OPA policy blocks production deployment of any AI employee monitoring or employment decision system in EU jurisdictions where works_council_consultation_status is 'consultation_not_yet_initiated', 'information_provided_awaiting_response', or 'deployment_blocked_by_works_council'.", "source": "German BetrVG § 87; EU Works Council Directive 94/45/EC; NLRA § 8; French CNIL guidance", "source_url": "https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A31994L0045" } ] }, { "subdomain": "Payroll, Benefits & Labour Compliance", "description": "Covers FLSA wage and hour compliance, benefits eligibility, and AI-driven payroll anomaly detection enumerations.", "relevant_standards": [ "FLSA (29 USC § 201) — Minimum wage, overtime, exempt/non-exempt classification", "ERISA (29 USC § 1001) — Employee benefit plan administration", "ACA (26 USC § 4980H) — Affordable Care Act employer mandate", "FMLA (29 USC § 2601) — Family and Medical Leave Act", "IRS Publication 15-T — Federal income tax withholding", "GDPR Article 9 — Health and financial data processed in payroll is sensitive personal data" ], "categories": [ { "enum_name": "WorkerClassificationStatus", "label": "Worker Classification Status", "otel_attribute": "hr.payroll.worker_classification", "opa_policy_path": "data.hr.payroll.worker_classification", "rego_input_key": "hr_payroll_worker_classification", "stability": "stable", "description": "FLSA and tax-law worker classification. AI workforce management agents must correctly classify workers before any pay calculation, benefit eligibility, or scheduling decision. Misclassification is one of the highest-exposure labour compliance risks.", "permitted_values": [ "employee_exempt_flsa", "employee_nonexempt_flsa", "employee_part_time", "employee_temporary", "independent_contractor_1099", "leased_worker_peo", "intern_paid", "intern_unpaid_compliant", "gig_worker_platform", "classification_under_review" ], "value_labels": { "employee_exempt_flsa": "Employee Exempt Flsa", "employee_nonexempt_flsa": "Employee Nonexempt Flsa", "employee_part_time": "Employee Part Time", "employee_temporary": "Employee Temporary", "independent_contractor_1099": "Independent Contractor 1099", "leased_worker_peo": "Leased Worker Peo", "intern_paid": "Intern Paid", "intern_unpaid_compliant": "Intern Unpaid Compliant", "gig_worker_platform": "Gig Worker Platform", "classification_under_review": "Classification Under Review" }, "code_definitions": { "employee_exempt_flsa": "Employee meets FLSA salary basis and duties tests for executive, administrative, or professional exemption; not entitled to overtime pay", "employee_nonexempt_flsa": "Employee subject to FLSA minimum wage and overtime (1.5x) requirements; AI scheduling agent must track hours to prevent overtime violations", "independent_contractor_1099": "Worker classified as independent contractor; employer has no FLSA, benefits, or payroll tax obligations; misclassification carries significant NLRB/IRS/DOL liability", "classification_under_review": "Worker classification is under legal or HR review; AI may not make scheduling or compensation decisions based on any single classification until review is complete" }, "regulatory_mappings": { "flsa": "FLSA — Non-exempt employees must receive minimum wage and overtime; AI scheduling must enforce hour tracking and overtime alerts", "irs_ss8": "IRS Form SS-8 — Worker classification determination; AI-assisted classification tools must apply the IRS common law test, ABC test (state dependent), or economic realities test", "california_ab5": "California AB5 / ABC test — Strict independent contractor classification criteria; AI workforce platforms must assess California AB5 compliance" }, "use_case": "AI workforce management agent checks worker classification before every scheduling and pay calculation action. 'Classification_under_review' status blocks AI from generating pay or scheduling recommendations until resolved. OPA policy alerts when an 'independent_contractor_1099' worker exceeds hours thresholds that suggest de facto employee status.", "source": "FLSA 29 USC § 201; IRS common law worker classification test; DOL ABC test guidance", "source_url": "https://www.dol.gov/agencies/whd/flsa" }, { "enum_name": "PayrollAnomalyType", "label": "Payroll Anomaly Type", "otel_attribute": "hr.payroll.anomaly_type", "opa_policy_path": "data.hr.payroll.anomaly_type", "rego_input_key": "hr_payroll_anomaly_type", "stability": "proposed", "description": "Type of payroll anomaly detected by an AI payroll audit agent. All anomalies require human payroll specialist review before correction — AI may not autonomously adjust payroll records.", "permitted_values": [ "overtime_threshold_exceeded", "minimum_wage_violation_suspected", "duplicate_payment", "ghost_employee_indicator", "unusual_pay_rate_change", "off_cycle_payment_unauthorised", "benefit_deduction_mismatch", "tax_withholding_error", "wage_garnishment_calculation_error", "pay_equity_outlier" ], "value_labels": { "overtime_threshold_exceeded": "Overtime Threshold Exceeded", "minimum_wage_violation_suspected": "Minimum Wage Violation Suspected", "duplicate_payment": "Duplicate Payment", "ghost_employee_indicator": "Ghost Employee Indicator", "unusual_pay_rate_change": "Unusual Pay Rate Change", "off_cycle_payment_unauthorised": "Off Cycle Payment Unauthorised", "benefit_deduction_mismatch": "Benefit Deduction Mismatch", "tax_withholding_error": "Tax Withholding Error", "wage_garnishment_calculation_error": "Wage Garnishment Calculation Error", "pay_equity_outlier": "Pay Equity Outlier" }, "regulatory_mappings": { "flsa": "FLSA — 'overtime_threshold_exceeded' and 'minimum_wage_violation_suspected' anomalies require immediate payroll correction; statute of limitations is 2 years (3 for wilful violations)", "eu_pay_transparency": "EU Pay Transparency Directive — 'pay_equity_outlier' anomaly may be evidence of unjustified gender pay gap; requires disclosure in joint pay assessment" }, "use_case": "AI payroll audit agent flags anomalies before each payroll run. 'Ghost_employee_indicator' and 'duplicate_payment' flags are escalated to Internal Audit as potential fraud indicators. OPA policy blocks payroll finalisation for any employee where a 'minimum_wage_violation_suspected' anomaly is open.", "source": "FLSA; EU Pay Transparency Directive; SOC 2 payroll audit control objectives; ACFE fraud detection patterns", "source_url": "https://www.dol.gov/agencies/whd/flsa" } ] } ], "opa_rego_policy_patterns": { "description": "Human Resources & Workforce Management-specific OPA Rego policy patterns referencing enum values from this file and from 00_core_sdk_and_governance.json. Illustrative patterns, not production policies.", "patterns": [ { "pattern_id": "hr.block_aedt_use_without_bias_audit_and_candidate_notice", "pattern_name": "block_aedt_use_without_bias_audit_and_candidate_notice", "enforcement_effect": "deny", "description": "Block any AI recruitment agent from using an Automated Employment Decision Tool output to influence a hiring decision unless: (1) a current independent bias audit is on file with no blocking findings, and (2) the candidate has received the required 10-business-day advance notice. Implements NYC Local Law 144 and EU AI Act Annex III para 4 simultaneously.", "applicable_enums": [ "RecruitmentStage", "BiasAuditOutcome", "CandidateNoticeStatus", "AIEmploymentDecisionType" ], "regulatory_basis": "NYC Local Law 144 (2021) — Annual bias audit and 10-business-day candidate notice required before AEDT use; EU AI Act Annex III para 4(a) — AI in recruitment is high-risk; Article 14 — Human oversight required; EEOC Uniform Guidelines 29 CFR Part 1607 — adverse impact analysis required", "rego_sketch": "package hr.recruitment\n\naedt_decision_types := {\n \"resume_ranking\",\n \"candidate_screening_pass_fail\",\n \"ai_video_interview_scoring\",\n \"skills_assessment_scoring\"\n}\n\npermitted_audit_outcomes := {\n \"compliant_no_disparate_impact\",\n \"compliant_with_conditions\"\n}\n\ndeny[msg] {\n input.hr_ai_decision_type in aedt_decision_types\n not input.hr_bias_audit_outcome in permitted_audit_outcomes\n msg := sprintf(\"NYC LL144 / EU AI Act Annex III para 4: AEDT '%v' cannot be used — bias audit outcome is '%v'. A current annual independent audit with no blocking findings is required.\", [input.hr_ai_decision_type, input.hr_bias_audit_outcome])\n}\n\ndeny[msg] {\n input.hr_ai_decision_type in aedt_decision_types\n input.hr_recruitment_candidate_notice_status != \"notice_window_elapsed_aedt_permitted\"\n msg := sprintf(\"NYC LL144: AEDT '%v' cannot be used for candidate '%v' — notice status is '%v'. 10 business days advance notice required before AEDT output influences any selection decision.\", [input.hr_ai_decision_type, input.candidate_id, input.hr_recruitment_candidate_notice_status])\n}\n\ndeny[msg] {\n input.hr_recruitment_candidate_notice_status == \"alternative_process_requested\"\n input.hr_ai_decision_type in aedt_decision_types\n msg := \"NYC LL144: Candidate has requested an alternative selection process. AEDT must not be used for this candidate until alternative process is completed.\"\n}" }, { "pattern_id": "hr.block_adverse_employment_decision_without_hitl", "pattern_name": "block_adverse_employment_decision_without_hitl", "enforcement_effect": "require_hitl_approval", "description": "Block any AI HR agent from autonomously issuing or finalising an adverse employment decision (termination, PIP initiation, demotion, involuntary reduction-in-force inclusion) without documented human HR and Legal review. EU AI Act Article 14 and GDPR Article 22 require human oversight for high-risk AI decisions with significant effects.", "applicable_enums": [ "AIEmploymentDecisionType", "PerformanceImprovementPlanStatus", "WorkforceReductionTriggerType" ], "regulatory_basis": "EU AI Act Annex III para 4 and Article 14 — Human oversight mandatory for all employment-related high-risk AI decisions; GDPR Article 22 — Employees have right not to be subject to solely automated decisions with significant effects; WARN Act — Mass layoff decisions require legal assessment before finalisation", "rego_sketch": "package hr.employment_decisions\n\nadverse_decision_types := {\n \"termination_risk_flag\",\n \"involuntary_separation_recommendation\",\n \"workforce_reduction_targeting\",\n \"performance_evaluation_ai_generated\"\n}\n\nwarn_act_triggers := {\n \"mass_layoff_us_warn_act\",\n \"site_closure\",\n \"collective_redundancy_eu\"\n}\n\ndeny[msg] {\n input.hr_ai_decision_type in adverse_decision_types\n not input.hrbp_hitl_reviewed == true\n msg := sprintf(\"EU AI Act Art 14 / GDPR Art 22: AI employment decision type '%v' requires human HR Business Partner review and approval. AI cannot autonomously issue adverse employment decisions.\", [input.hr_ai_decision_type])\n}\n\ndeny[msg] {\n input.hr_workforce_reduction_trigger_type in warn_act_triggers\n not input.legal_hitl_warn_assessment_completed == true\n msg := sprintf(\"WARN Act / EU Collective Redundancy Directive: Workforce reduction type '%v' requires Legal assessment of notice and consultation obligations before the reduction list can be finalised.\", [input.hr_workforce_reduction_trigger_type])\n}\n\ndeny[msg] {\n input.hr_performance_pip_status == \"pip_initiated_employee_notified\"\n not input.hrbp_hitl_approved == true\n msg := \"EU AI Act Annex III para 4 / GDPR Art 22: PIP initiation cannot be based solely on AI performance flag. Human HRBP review and approval required before employee is notified.\"\n}" }, { "pattern_id": "hr.enforce_works_council_consultation_gate_eu", "pattern_name": "enforce_works_council_consultation_gate_eu", "enforcement_effect": "deny", "description": "Block production deployment of any AI employee monitoring or employment decision system in EU jurisdictions where works council consultation has not been completed. Covers German BetrVG co-determination rights, EU Works Council Directive transnational consultation, and NLRA collective bargaining obligations.", "applicable_enums": [ "WorksCouncilConsultationStatus", "EmployeeMonitoringType", "AIEmploymentDecisionType" ], "regulatory_basis": "German BetrVG § 87(1)(6) — Works council co-determination on monitoring; EU Works Council Directive 94/45/EC — transnational information and consultation; NLRA § 8(a)(5) — good faith bargaining obligation for AI affecting working conditions", "rego_sketch": "package hr.works_council\n\nblocking_consultation_statuses := {\n \"consultation_not_yet_initiated\",\n \"information_provided_awaiting_response\",\n \"consultation_in_progress\",\n \"deployment_blocked_by_works_council\"\n}\n\neu_monitoring_types := {\n \"productivity_keystroke_mouse_activity\",\n \"video_surveillance_workplace\",\n \"video_surveillance_remote_work\",\n \"email_communication_monitoring\",\n \"communication_sentiment_analysis\",\n \"ai_meeting_transcription_analysis\"\n}\n\ndeny[msg] {\n input.deployment_jurisdiction in {\"DE\", \"FR\", \"NL\", \"AT\", \"BE\", \"EU_transnational\"}\n input.hr_monitoring_type in eu_monitoring_types\n input.hr_works_council_consultation_status in blocking_consultation_statuses\n msg := sprintf(\"Works Council / BetrVG § 87: AI monitoring type '%v' cannot be deployed in jurisdiction '%v' — works council consultation status is '%v'. Deployment blocked until consultation is completed with agreement.\", [input.hr_monitoring_type, input.deployment_jurisdiction, input.hr_works_council_consultation_status])\n}\n\ndeny[msg] {\n input.hr_works_council_consultation_status == \"deployment_blocked_by_works_council\"\n msg := \"BetrVG § 87 / EU Works Council: Works council has exercised co-determination right to block deployment. Legal proceedings or conciliation required before this AI system may operate.\"\n}" }, { "pattern_id": "hr.enforce_eu_pay_transparency_joint_assessment", "pattern_name": "enforce_eu_pay_transparency_joint_assessment", "enforcement_effect": "deny", "description": "Trigger mandatory works council notification and joint pay assessment workflow when AI pay equity analysis identifies a gender pay gap exceeding 5% that cannot be justified by gender-neutral criteria, as required by EU Pay Transparency Directive Article 9.", "applicable_enums": [ "PayEquityAnalysisOutcome", "CompensationBandingStatus", "AIEmploymentDecisionType" ], "regulatory_basis": "EU Pay Transparency Directive (2023/970) Article 9 — Joint pay assessment mandatory where gender pay gap exceeds 5% and cannot be justified; transposition deadline June 7, 2026; Article 7 — Right of workers to request pay comparison information", "rego_sketch": "package hr.pay_equity\n\njoint_assessment_triggers := {\n \"gap_identified_5_to_20pct_review_required\",\n \"gap_identified_above_20pct_assessment_mandatory\"\n}\n\ndeny[msg] {\n input.hr_pay_equity_analysis_outcome in joint_assessment_triggers\n not input.works_council_pay_assessment_initiated == true\n msg := sprintf(\"EU Pay Transparency Directive Art 9: Pay equity analysis outcome '%v' requires initiation of joint pay assessment with workers' representatives. Assessment must be completed and remediation plan agreed within 6 months.\", [input.hr_pay_equity_analysis_outcome])\n}\n\ndeny[msg] {\n input.hr_ai_decision_type == \"compensation_adjustment_recommendation\"\n input.hr_pay_equity_analysis_outcome == \"analysis_in_progress\"\n msg := \"EU Pay Transparency Directive: Compensation adjustment recommendations blocked while pay equity analysis is in progress. Analysis must complete before merit cycle AI recommendations are generated.\"\n}" }, { "pattern_id": "hr.block_biometric_emotion_inference_monitoring", "pattern_name": "block_biometric_emotion_inference_monitoring", "enforcement_effect": "deny", "description": "Block any AI HR monitoring agent from using biometric fatigue detection or emotion inference to inform employment decisions. EU AI Act Article 5(1)(f) prohibits AI systems that infer emotions from biometrics in employment contexts, with narrow exceptions for safety monitoring. Ambient listening and always-on video surveillance require specific legal basis.", "applicable_enums": [ "EmployeeMonitoringType", "AIEmploymentDecisionType", "BiasAuditOutcome" ], "regulatory_basis": "EU AI Act Article 5(1)(f) — Prohibition on AI systems that infer emotions of natural persons in the workplace except for safety or medical reasons; GDPR Article 9 — Biometric data is special category requiring explicit consent or substantial public interest; Article 5(1)(a) — Prohibition on AI exploiting workers' vulnerabilities", "rego_sketch": "package hr.monitoring_compliance\n\nprohibited_monitoring_types := {\n \"biometric_fatigue_detection\",\n \"ambient_listening_office\"\n}\n\nrestricted_monitoring_types := {\n \"communication_sentiment_analysis\",\n \"video_surveillance_remote_work\"\n}\n\ndeny[msg] {\n input.hr_monitoring_type in prohibited_monitoring_types\n msg := sprintf(\"EU AI Act Article 5(1)(f) / GDPR Article 9: Monitoring type '%v' is prohibited or requires specific legal basis not applicable in standard employment context. Deployment blocked.\", [input.hr_monitoring_type])\n}\n\ndeny[msg] {\n input.hr_monitoring_type in restricted_monitoring_types\n not input.gdpr_lawful_basis_documented == true\n not input.works_council_agreement_on_file == true\n msg := sprintf(\"GDPR / Works Council: Monitoring type '%v' requires documented GDPR lawful basis AND works council agreement before deployment. One or both are missing.\", [input.hr_monitoring_type])\n}\n\ndeny[msg] {\n input.hr_monitoring_type == \"communication_sentiment_analysis\"\n input.hr_ai_decision_type in {\"termination_risk_flag\", \"involuntary_separation_recommendation\"}\n msg := \"EU AI Act Art 5(1)(a) / NLRA: Communication sentiment analysis data cannot feed termination or separation AI decisions. This use constitutes exploitation of worker vulnerabilities and may interfere with protected concerted activity.\"\n}" } ] }, "agent_registry_fields": { "description": "Recommended fields for registering a human resources or workforce management domain agentic AI system in the GRC portal. Supplements the core agent identity schema from 00_core_sdk_and_governance.json.", "fields": [ { "field": "eu_ai_act_annex3_employment_category", "type": "string", "description": "EU AI Act Annex III para 4 sub-category applicable to this HR AI system. Drives full high-risk AI compliance obligations from August 2, 2026. Use values such as 4a_i_recruitment_selection, 4a_ii_task_allocation_monitoring, 4a_iii_promotion_career, 4a_iv_termination, 4b_self_employment_access, or not_in_scope.", "required_when": "All AI systems used in recruitment, selection, task allocation, employee monitoring, promotion, or termination decisions affecting EU-based workers" }, { "field": "nyc_ll144_aedt_registered", "type": "boolean", "description": "True if this AI system meets the NYC Local Law 144 definition of an Automated Employment Decision Tool and has an annual independent bias audit on file with results publicly posted.", "required_when": "All AI recruitment and selection tools used for NYC-based employment decisions" }, { "field": "bias_audit_expiration_date", "type": "string", "description": "Expiration date of the most recent independent bias audit. NYC LL144 requires annual audits; audit expiration blocks continued AEDT use.", "required_when": "All AEDTs subject to NYC LL144 or equivalent state law bias audit requirements" }, { "field": "works_council_agreement_reference", "type": "string", "description": "Reference number of the works council agreement (Betriebsvereinbarung or equivalent) governing the use of this AI system for employee monitoring or employment decisions. Required in EU jurisdictions with co-determination rights.", "required_when": "All HR AI monitoring and employment decision systems deployed in EU member states with works council co-determination rights (DE, AT, NL, FR, BE and others)" }, { "field": "eeoc_validity_evidence_type", "type": "string", "description": "Type of validity evidence supporting this AI selection tool per EEOC Uniform Guidelines 29 CFR Part 1607. Required for all AI tools used in selection decisions that may have adverse impact. Use values such as criterion_related_validity, content_validity, construct_validity, synthetic_validity, or no_validity_study_conducted.", "required_when": "All AI assessment, screening, and selection tools used in employment decisions" }, { "field": "pay_transparency_disclosure_enabled", "type": "boolean", "description": "True if this AI compensation tool supports EU Pay Transparency Directive Article 7 requirements — providing employees with information about salary levels for comparable work categories.", "required_when": "AI compensation management agents deployed for EU employers with 100+ employees" }, { "field": "employee_data_processing_basis", "type": "string", "description": "GDPR lawful basis for processing employee personal data by this AI system. Employment data processing under GDPR typically relies on Article 6(1)(b) (contract performance), 6(1)(c) (legal obligation), or 6(1)(f) (legitimate interests) — explicit consent is rarely appropriate in an employment relationship. Use values such as 6_1_b_contract_performance, 6_1_c_legal_obligation, 6_1_f_legitimate_interests, 9_2_b_employment_law, or not_processing_eu_employee_data.", "required_when": "All AI agents processing personal data of EU-based employees" }, { "field": "illinois_aivia_consent_mechanism", "type": "boolean", "description": "True if this AI video interview analysis tool obtains advance written consent from interviewees per Illinois AIVIA and conducts annual racial bias testing. Required for Illinois-based employment interviews.", "required_when": "AI video interview analysis tools used for Illinois-based candidates" } ] } }