eu-ai-act-specialist

name	eu-ai-act-specialist
description	EU AI Act (Regulation EU 2024/1689) compliance specialist. Use when classifying AI systems by risk tier, assessing provider or deployer obligations, evaluating GPAI model compliance, running conformity assessments, performing bias detection and fairness testing, building AI governance programs, or preparing for EU AI Act enforcement deadlines. Covers the full regulatory lifecycle from system inventory through post-market monitoring.
license	MIT + Commons Clause
metadata	{"version":"1.0.0","author":"borghei","category":"compliance","domain":"ai-governance","updated":"2026-03-31T00:00:00.000Z","tags":["eu-ai-act","ai-governance","risk-classification","gpai","conformity"]}

EU AI Act Compliance Specialist

Production-ready compliance patterns for Regulation (EU) 2024/1689 -- the EU Artificial Intelligence Act. Covers risk classification, provider/deployer obligations, GPAI model requirements, conformity assessment, and AI governance.

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AI System Inventory and Classification Workflow

The agent classifies AI systems under the EU AI Act's risk-based framework and maps applicable obligations.

Workflow: Classify and Map Obligations

1. Inventory all AI systems -- for each system, document: name, provider/developer, description, intended purpose, deployment status, affected persons, geographic scope, data processed, and decision impact level.
2. Apply classification decision tree to each system:
   • Does it meet the Art. 3(1) definition of an AI system? If no, document exclusion.
   • Does it fall under a prohibited practice (Art. 5)? If yes, flag as UNACCEPTABLE RISK -- must be discontinued.
   • Is it a safety component of an Annex I product? If yes, HIGH-RISK (product legislation path).
   • Does it fall under an Annex III category? If yes, apply Art. 6(3) exception analysis. If exception does not apply, HIGH-RISK.
   • Does Art. 50 transparency obligation apply? If yes, LIMITED RISK. Otherwise, MINIMAL RISK.
3. Map obligations based on classification -- assign compliance owners for each obligation.
4. Run gap analysis using scripts/ai_compliance_checker.py to identify compliance gaps.
5. Prioritize remediation by deadline urgency, penalty severity, and number of affected persons.
6. Validation checkpoint: Every AI system classified; prohibited practices flagged for immediate action; high-risk systems have assigned compliance owners and remediation timelines.

Example: AI System Classification Output

{
  "system_name": "Resume Screener v2.1",
  "provider": "Internal ML Team",
  "intended_purpose": "Screen job applications and rank candidates for recruiter review",
  "ai_act_classification": "HIGH-RISK",
  "classification_rationale": "Annex III Category 4 - Employment: AI for recruitment and screening of job applicants",
  "art_6_3_exception": false,
  "exception_rationale": "System directly influences which candidates proceed to interview stage - not a narrow procedural task",
  "applicable_obligations": [
    "Risk management system (Art. 9)",
    "Data governance (Art. 10)",
    "Technical documentation (Art. 11)",
    "Record-keeping / automatic logging (Art. 12)",
    "Transparency and information to deployers (Art. 13)",
    "Human oversight (Art. 14)",
    "Accuracy, robustness, cybersecurity (Art. 15)",
    "Quality management system (Art. 17)"
    
    
    
    
  
   
   

---

Risk Classification System

The AI Act uses a risk-based approach with four tiers.

Tier 1: Prohibited Practices (Art. 5) -- Banned from 2 February 2025

Prohibited Practice	Article
Social scoring by public authorities	Art. 5(1)(c)
Real-time remote biometric identification in public spaces (with narrow exceptions)	Art. 5(1)(h)
Emotion recognition in workplace and education (except medical/safety)	Art. 5(1)(f)
Individual predictive policing based solely on profiling	Art. 5(1)(d)
Exploitation of vulnerabilities (age, disability, social/economic situation)	Art. 5(1)(b)
Subliminal manipulation causing significant harm	Art. 5(1)(a)
Untargeted facial image scraping for recognition databases	Art. 5(1)(e)
Biometric categorization by sensitive attributes (race, religion, etc.)	Art. 5(1)(g)

Tier 2: High-Risk AI Systems (Art. 6, Annex III)

An AI system is high-risk if it falls under Annex III categories OR is a safety component of a product covered by Annex I harmonization legislation.

Annex III Categories:

#	Category	Examples
1	Biometric identification and categorization	Remote biometric ID, emotion recognition
2	Critical infrastructure management	Road traffic, water/gas/electricity supply, digital infrastructure
3	Education and vocational training	Admissions, learning outcome evaluation, test monitoring
4	Employment and workers management	Recruitment/screening, promotion/termination, performance monitoring
5	Essential private and public services	Creditworthiness, insurance risk, public assistance eligibility
6	Law enforcement	Polygraph, deepfake detection, crime analytics
7	Migration, asylum, border control	Asylum risk assessment, visa/permit examination
8	Administration of justice	Judicial fact-finding, election influence

Tier 3: Limited Risk -- Transparency Obligations (Art. 50)

System Type	Transparency Requirement
Chatbots / AI interacting with persons	Inform person they are interacting with AI
Emotion recognition / biometric categorization	Inform exposed persons of system operation
Deepfakes / AI-generated content	Disclose AI generation; machine-readable labelling
AI-generated text on public interest matters	Disclose AI generation unless editorially reviewed

Tier 4: Minimal Risk

No mandatory requirements. Voluntary codes of conduct encouraged (Art. 95).

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Provider Obligations for High-Risk AI

Providers of high-risk AI systems must comply with all of the following:

#	Obligation	Article	Key Requirement
1	Risk Management System	Art. 9	Continuous iterative process throughout lifecycle; test against defined metrics
2	Data Governance	Art. 10	Training/validation/testing datasets meet quality, representativeness, and bias criteria
3	Technical Documentation	Art. 11	Drawn up before market placement; kept up to date throughout lifecycle
4	Record-Keeping / Logging	Art. 12	Automatic recording of events enabling traceability
5	Transparency	Art. 13	Instructions for use with capabilities, limitations, and oversight measures
6	Human Oversight	Art. 14	Human-in-the-loop, on-the-loop, or in-command depending on risk
7	Accuracy, Robustness, Cybersecurity	Art. 15	Appropriate levels declared and maintained; adversarial resilience
8	Quality Management System	Art. 17	Documented QMS covering design, development, testing, data management, post-market
9	Conformity Assessment	Art. 43	Internal control (Annex VI) or third-party assessment (Annex VII)
10	CE Marking	Art. 48	Affix CE marking before market placement
11	EU Database Registration	Art. 49	Register in EU database before market placement
12	Post-Market Monitoring	Art. 72	Active systematic data collection; serious incident reporting within 15 days

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Deployer Obligations (Art. 26)

Obligation	Detail
Use per instructions	Operate per provider's instructions for use
Human oversight	Assign competent, trained, authorized oversight personnel
Input data relevance	Ensure input data is relevant and representative
Monitoring	Monitor operation; inform provider of risks/incidents
Record-keeping	Keep auto-generated logs (minimum 6 months)
Inform workers	Notify workers/representatives before deployment of high-risk AI
DPIA	Carry out GDPR Art. 35 data protection impact assessment when required
Fundamental Rights Impact Assessment	Required for public bodies / private entities providing public services (Art. 27)

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General-Purpose AI Models (GPAI)

GPAI Provider Obligations (Art. 53) -- Effective 2 August 2025

Obligation	Detail
Technical documentation	Maintain documentation of model training/testing process
Information for downstream	Provide sufficient info for downstream AI system providers
Copyright compliance	Comply with EU copyright law; honor opt-out mechanisms
Training data summary	Publish detailed summary of training content per AI Office template
EU representative	Non-EU providers must appoint EU-based representative

Systemic Risk GPAI Models (Art. 51, 55)

Classified as systemic risk if: high impact capabilities, AI Office designation, or trained with >10^25 FLOPs (rebuttable presumption).

Additional obligations: Model evaluation with adversarial testing, red-teaming proportionate to risk, systemic risk assessment and mitigation, incident tracking and reporting, cybersecurity protection, energy consumption reporting.

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Conformity Assessment Workflow

The agent guides organizations through conformity assessment for high-risk AI systems.

Workflow: Internal Control (Annex VI)

1. Establish QMS per Art. 17 -- document design, development, testing, data management, and post-market monitoring processes.
2. Compile technical documentation per Art. 11 -- system description, development process, risk management, data governance, performance metrics.
3. Implement all Chapter III Section 2 requirements -- verify each obligation is addressed.
4. Conduct internal assessment:
   • Verify risk management system addresses all identified risks (Art. 9)
   • Verify data governance meets Art. 10 requirements
   • Verify technical documentation is complete and current (Art. 11)
   • Verify logging capability (Art. 12)
   • Verify transparency and instructions for use (Art. 13)
   • Verify human oversight design (Art. 14)
   • Verify accuracy, robustness, cybersecurity (Art. 15)
   • Confirm QMS covers all required elements (Art. 17)
5. Sign EU Declaration of Conformity (Art. 47), affix CE marking (Art. 48), register in EU database (Art. 49).
6. Implement post-market monitoring (Art. 72) and maintain documentation updates.
7. Validation checkpoint: All 12 provider obligations verified; declaration signed; CE marking affixed; EU database registration complete; post-market monitoring operational.

Workflow: Third-Party Assessment (Annex VII)

Required for biometric identification systems (Annex III point 1) and cases where harmonized standards are insufficient.

1. Complete all internal control steps above.
2. Select and engage notified body with relevant AI system expertise.
3. QMS assessment -- notified body reviews and assesses QMS; issues certificate or requires corrective action; annual surveillance.
4. Technical documentation assessment -- notified body reviews documentation, tests system, issues type-examination certificate.
5. Sign EU Declaration of Conformity with notified body identification number on CE marking.
6. Maintain ongoing compliance -- notified body surveillance, notify of significant changes, maintain all documentation.
7. Validation checkpoint: Notified body certificates issued; CE marking with NB number affixed; ongoing surveillance scheduled.

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Bias Detection and Fairness Testing

The agent performs bias detection per Art. 10 data governance requirements.

Workflow: Bias Testing

1. Define protected attributes -- age, gender, ethnicity, disability, religion, and other relevant characteristics for the system's context.
2. Analyze data distribution -- check representation ratios (target: 0.8-1.25 vs. population), class imbalance ratios (>0.5), and coverage of all known groups.
3. Evaluate outcome fairness using these metrics:
   • Demographic parity: P(positive outcome) equal across groups (within 80% / four-fifths rule)
   • Equalized odds: TPR and FPR equal across groups (within 80%)
   • Predictive parity: PPV equal across groups (within 80%)
   • Calibration: Predicted probabilities accurate for all groups (within 5pp)
4. Identify proxy variables -- check for features correlated with protected attributes.
5. Implement mitigation -- data augmentation, re-sampling, re-weighting, adversarial debiasing, threshold adjustment, or reject option classification as appropriate.
6. Validate -- re-run analysis to confirm improvement.
7. Document -- record all findings, measures taken, and residual bias levels in technical documentation.
8. Validation checkpoint: All protected attributes tested; fairness metrics within thresholds or residual bias documented with justification; mitigation measures recorded.

Example: Bias Detection Command

# Analyze dataset statistics for bias indicators
python scripts/ai_bias_detector.py --input dataset_stats.json \
  --protected-attributes gender,age_group,ethnicity

# Output as JSON for integration with compliance documentation
python scripts/ai_bias_detector.py --input dataset_stats.json --json

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Implementation Timeline

Date	Milestone	Key Requirements
1 Aug 2024	Entry into force	Regulation published
2 Feb 2025	Prohibited practices + AI literacy	Art. 5 prohibitions; Art. 4 AI literacy
2 Aug 2025	GPAI obligations + governance	Art. 53, 55 GPAI obligations; AI Office operational
2 Aug 2026	Full application	All remaining: high-risk, deployer, transparency, conformity, CE marking
2 Aug 2027	Extended deadline	Certain Annex I Section B high-risk safety components

Penalties (Art. 99)

Violation Type	Maximum Fine	% Global Turnover
Prohibited AI practices	EUR 35 million	7% (whichever higher)
High-risk non-compliance	EUR 15 million	3% (whichever higher)
Misleading information to authorities	EUR 7.5 million	1% (whichever higher)

SMEs and startups receive proportionate treatment (lower of absolute or percentage).

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AI Model Documentation Templates

Template: AI System Description

AI SYSTEM DESCRIPTION
=====================
System Name:
Version:
Provider:
Date:

1. GENERAL INFORMATION
   - Intended purpose:
   - Target users (deployers):
   - Affected persons:
   - Geographic scope:
   - AI Act classification:
   - Annex III category (if applicable):

2. TECHNICAL ARCHITECTURE
   - Model type:
   - Input data modalities:
   - Output description:
   - Key design choices and rationale:

3. TRAINING AND DATA
   - Training data sources:
   - Data volume and characteristics:
   - Data preparation methods:
   - Bias examination results:

4. PERFORMANCE
   - Accuracy metrics:
   - Robustness testing results:
   - Known limitations:
   - Performance across demographic groups:

5. HUMAN OVERSIGHT
   - Oversight level: [human-in-the-loop / on-the-loop / in-command]
   - Override mechanism:
   - Automation bias safeguards:

Template: Risk Management Documentation

RISK MANAGEMENT SYSTEM -- AI SYSTEM
====================================
System Name:
Version:
Risk Management Lead:
Date:

1. RISK IDENTIFICATION
   | Risk ID | Description | Likelihood | Severity | Risk Level |
   |---------|-------------|------------|----------|------------|
   | R-001   |             |            |          |            |

2. RISK CONTROL MEASURES
   | Risk ID | Measure | Type | Verification | Status |
   |---------|---------|------|-------------|--------|
   | R-001   |         |      |             |        |

3. RESIDUAL RISK ASSESSMENT
   - Acceptability determination:
   - Overall risk-benefit analysis:

4. POST-MARKET DATA INTEGRATION
   - Review frequency:
   - Trigger conditions for update:

---

Tools

AI Risk Classifier

# Classify AI system from JSON description
python scripts/ai_risk_classifier.py --input system_description.json

# Classify from inline JSON
python scripts/ai_risk_classifier.py --inline '{
  "name": "Resume Screener",
  "description": "AI system that screens job applications and ranks candidates",
  "domain": "employment",
  "uses_biometrics": false,
  "decision_type": "automated_with_review",
  "affected_persons": "job applicants",
  "eu_deployment": true
}'

# JSON output for programmatic use
python scripts/ai_risk_classifier.py --input system.json --json

AI Compliance Checker

# Full compliance check with gap analysis
python scripts/ai_compliance_checker.py --input compliance_status.json

# Check deployer obligations only
python scripts/ai_compliance_checker.py --input compliance_status.json --role deployer

# JSON output with remediation steps
python scripts/ai_compliance_checker.py --input compliance_status.json --json

AI Bias Detector

# Analyze dataset for bias indicators mapped to Art. 10
python scripts/ai_bias_detector.py --input dataset_stats.json

# Specify protected attributes explicitly
python scripts/ai_bias_detector.py --input dataset_stats.json \
  --protected-attributes gender,age_group,ethnicity --json

---

Reference Documentation

Document	Path	Description
Classification Guide	references/ai-act-classification-guide.md	Complete Annex III categories, decision trees, prohibited practices, GPAI classification
Governance Framework	references/ai-governance-framework.md	Organizational structure, ethics board, model lifecycle, conformity assessment procedures
Documentation Templates	references/ai-technical-documentation-templates.md	Full templates for system description, risk management, data governance, testing, oversight, post-market monitoring, incident reporting, FRIA

---

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Troubleshooting

Problem	Possible Cause	Resolution
AI system classified as HIGH-RISK but organization believes it qualifies for Art. 6(3) exception	Exception analysis incomplete or domain mapping incorrect	Re-evaluate against all Art. 6(3) exception criteria; the system must perform a narrow procedural task, improve the result of a previously completed human activity, or be purely preparatory; document rationale with legal review
Bias detector reports disparate impact but model performs well overall	Aggregated metrics mask subgroup disparities; four-fifths rule violation on specific protected attributes	Analyze per-group positive outcome rates using --protected-attributes flag; implement targeted mitigation (re-sampling, threshold adjustment) for affected groups; document residual bias with justification
Compliance checker returns low score despite extensive documentation	Documentation exists but key compliance fields marked as incomplete or not up to date	Verify each obligation field in the input JSON reflects current state; ensure kept_up_to_date and lifecycle_coverage flags are set; update technical documentation per Art. 11 before reassessment
System falls under multiple Annex III categories simultaneously	AI system serves multiple domains (e.g., employment + education)	Classify under the highest-risk applicable category; apply the most stringent obligations; document classification rationale for each category
GPAI model obligations unclear for downstream provider	Upstream GPAI provider has not supplied sufficient documentation per Art. 53	Request technical documentation, training data summary, and copyright compliance information from the GPAI provider; if unavailable, document the gap and assess independent obligations
Conformity assessment route uncertain (internal vs. third-party)	Biometric identification system or insufficient harmonized standards	Biometric ID systems (Annex III point 1) require third-party assessment (Annex VII); all others may use internal control (Annex VI) unless harmonized standards are unavailable; consult notified body
Post-market monitoring shows model performance degradation	Data drift, concept drift, or deployment context changed since initial assessment	Trigger Art. 72 post-market monitoring procedures; report serious incidents within 15 days; update risk management system and technical documentation; consider re-running conformity assessment

---

Success Criteria

• All AI systems inventoried and classified -- every system assessed against the risk-based framework with documented classification rationale, including Art. 6(3) exception analysis where applicable
• Prohibited practices identified and discontinued -- all Art. 5 prohibited practices flagged by February 2, 2025, with documented evidence of discontinuation or lawful exception application
• High-risk systems fully compliant by August 2, 2026 -- all 12 provider obligations verified, EU Declaration of Conformity signed, CE marking affixed, and EU database registration complete
• Bias testing completed for all high-risk systems -- demographic parity, equalized odds, and predictive parity metrics within four-fifths threshold for all protected attributes, or residual bias documented with justification
• GPAI model obligations met by August 2, 2025 -- technical documentation maintained, downstream provider information supplied, copyright compliance verified, and training data summary published
• Conformity assessment completed per correct route -- internal control (Annex VI) or third-party assessment (Annex VII) selected based on system classification, with all certificates issued and filed

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Scope & Limitations

In Scope:

• AI system risk classification across all four tiers (Prohibited, High-Risk, Limited Risk, Minimal Risk)
• Annex III category analysis and Art. 6(3) exception evaluation
• Provider and deployer obligation mapping with compliance gap analysis
• GPAI model classification including systemic risk determination (10^25 FLOPs threshold)
• Bias detection and fairness testing mapped to Art. 10 data governance requirements
• Conformity assessment workflow guidance (Annex VI internal control and Annex VII third-party)
• Implementation timeline tracking with penalty exposure assessment

Out of Scope:

• Actual ML model training, retraining, or adversarial testing -- this skill provides compliance frameworks, not ML engineering tools
• Notified body selection, engagement, or audit execution
• National regulatory sandbox applications or experimental AI system exemptions
• Detailed GPAI Code of Practice implementation beyond obligation mapping
• CE marking physical affixation or EU database registration system interaction
• Legal advice on liability, insurance, or contractual allocation of AI Act obligations

Important Notes:

• The EU AI Act compliance deadline of August 2, 2026 for high-risk systems is firm -- organizations should begin classification and gap analysis immediately
• Penalties are severe: up to EUR 35 million or 7% of global turnover for prohibited practices, EUR 15 million or 3% for high-risk non-compliance
• SMEs and startups receive proportionate penalty treatment (lower of absolute or percentage)

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Integration Points

Skill	Integration	When to Use
iso42001-ai-management	ISO 42001 AIMS provides organizational framework for EU AI Act compliance; certification demonstrates Art. 17 QMS	When building AI governance program that satisfies both ISO 42001 and EU AI Act
gdpr-dsgvo-expert	Art. 10 data governance overlaps with GDPR; high-risk AI systems processing personal data require DPIA per GDPR Art. 35	When AI system processes personal data and requires combined DPIA + conformity assessment
mdr-745-specialist	AI medical devices fall under both EU AI Act and MDR; MDR conformity assessment may satisfy AI Act per Art. 120	When AI-enabled medical device requires dual MDR and AI Act compliance
fda-consultant-specialist	Cross-jurisdictional AI/ML SaMD compliance mapping between FDA PCCP and EU AI Act	When AI medical device is marketed in both US and EU
infrastructure-compliance-auditor	Technical security controls supporting Art. 15 accuracy, robustness, and cybersecurity requirements	When validating infrastructure security for deployed high-risk AI systems

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Tool Reference

ai_risk_classifier.py

Classifies AI systems into EU AI Act risk categories based on a JSON system description.

Flag	Required	Description
--input <file>	Yes (unless --inline)	Path to JSON file containing AI system description
--inline '<json>'	No	Inline JSON system description for quick classification
--json	No	Output results in JSON format for programmatic use
--output <file>	No	Export classification report to specified file path

Input Fields: name, description, domain, sub_domain, uses_biometrics, biometric_type, biometric_context, interacts_with_persons, generates_content, content_type, decision_type, affected_persons, is_safety_component, product_legislation, eu_deployment, social_scoring, manipulates_behavior, targets_vulnerable_groups, predictive_policing_individual, untargeted_scraping, is_gpai, training_compute_flops, critical_infrastructure, infrastructure_type.

ai_compliance_checker.py

Validates AI system compliance against all provider and deployer obligations with gap analysis.

Flag	Required	Description
--input <file>	Yes	Path to JSON compliance status file
--role <role>	No	Check obligations for specific role: provider (default) or deployer
--json	No	Output results in JSON format with remediation steps
--output <file>	No	Export compliance report to specified file path

Output: Overall compliance score (0-100), per-obligation status, gap analysis with Art. references, and prioritized remediation recommendations.

ai_bias_detector.py

Analyzes dataset statistics for bias indicators mapped to Art. 10 data governance requirements.

Flag	Required	Description
--input <file>	Yes	Path to JSON file with dataset statistics (demographics, outcomes, correlations)
--protected-attributes <attrs>	No	Comma-separated list of protected attributes to analyze (e.g., gender,age_group,ethnicity)
--json	No	Output results in JSON format
--output <file>	No	Export bias assessment report to specified file path

Thresholds: Representation ratio 0.8-1.25 (within 20% of population), class imbalance >0.5, four-fifths rule (0.8) for disparate impact, proxy correlation >0.5 for proxy variable detection.

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Regulation Reference: Regulation (EU) 2024/1689 of the European Parliament and of the Council of 13 June 2024 Last Updated: March 2026 Version: 1.0.0