EU AI Act High-Risk AI System Documentation

Overview

The EU AI Act (Regulation 2024/1689, entered into force 1 August 2024, with high-risk obligations applicable from 2 August 2026) establishes a risk-based regulatory framework for artificial intelligence systems. High-risk AI systems — those listed in Annex III or used as safety components of products covered by Union harmonisation legislation in Annex I — must meet extensive documentation, transparency, and governance requirements before being placed on the EU market. Cerebrum AI Labs must prepare comprehensive technical documentation, implement a risk management system, ensure data governance, and undergo conformity assessment for each high-risk AI system.

High-Risk Classification

Annex III Categories Relevant to Cerebrum AI Labs

Category	Annex III Reference	Cerebrum AI Labs System	Classification
Employment and workers management	Annex III, para. 4(a)	CV Screening AI — automated filtering of job applications	High-risk
Access to essential services	Annex III, para. 5(b)	Credit Scoring AI — creditworthiness assessment for financial products	High-risk
Law enforcement	Annex III, para. 6(a)	Not applicable	N/A
Biometric identification	Annex III, para. 1(a)	Facial Verification AI — identity verification at onboarding	High-risk
Education and vocational training	Annex III, para. 3(a)	Not applicable	N/A

Classification Decision Tree

Is the AI system listed in Annex III?
├── Yes → High-risk (unless exception applies under Art. 6(3))
│   └── Does the system make decisions materially affecting natural persons?
│       ├── Yes → High-risk confirmed
│       └── No → May qualify for Art. 6(3) exception (narrow, profiling, preparatory)
│
├── No → Is it a safety component of a product under Annex I legislation?
│   ├── Yes → High-risk (subject to third-party conformity assessment)
│   └── No → Not high-risk under AI Act
│
└── Is it a general-purpose AI model with systemic risk? (Art. 51)
    ├── Yes → GPAI systemic risk obligations
    └── No → GPAI transparency obligations only

Technical Documentation Requirements (Art. 11)

Annex IV — Required Documentation Content

Section 1: General Description

Document Element	Content for Cerebrum AI Labs CV Screening AI
Intended purpose	Automated screening and ranking of job applications based on qualification match
Provider name and contact	Cerebrum AI Labs, 42 Innovation Drive, Dublin, Ireland
AI system version	v2.4.1 (deployed March 2026)
Hardware/software requirements	Cloud-hosted on EU infrastructure (AWS eu-west-1), Python 3.11, PyTorch 2.2
Product integration	Integrated into Cerebrum TalentFlow ATS platform

Section 2: Detailed Description of System Elements

Element	Documentation Required
Development methodology	Model architecture, training approach, design choices and rationale
Computational resources	Training compute (GPU hours), energy consumption
Training data	Data sources, collection methods, size, labeling methodology, preprocessing
Validation and testing	Test datasets, metrics, results, known limitations
Input data specifications	Expected input format, quality requirements
Output description	Output format, confidence scores, decision thresholds

Section 3: Monitoring, Functioning, and Control

Element	Documentation Required
Human oversight measures	Art. 14 requirements: override capability, decision review process
Technical measures for accuracy	Accuracy metrics, drift detection, retraining triggers
Cybersecurity measures	Data encryption, access controls, adversarial robustness testing
Performance in edge cases	Known failure modes, boundary conditions, degradation behavior

Section 4: Risk Management

Element	Documentation Required
Risk management system	Art. 9 risk management process documentation
Known and foreseeable risks	Risk register with severity and likelihood
Mitigation measures	Controls for each identified risk
Residual risk assessment	Acceptable residual risk justification

Risk Management System (Art. 9)

Continuous Risk Management Process for Cerebrum AI Labs

Phase	Activity	Frequency
Identification	Identify risks to health, safety, and fundamental rights	Initial + quarterly
Analysis	Estimate risk severity and likelihood	Initial + quarterly
Evaluation	Compare risks against acceptance criteria	Initial + quarterly
Mitigation	Implement risk reduction measures	Ongoing
Monitoring	Track risk indicators in production	Continuous
Review	Review and update risk assessment	Quarterly

Risk Register — CV Screening AI

Risk ID	Risk Description	Severity	Likelihood	Mitigation	Residual Risk
R-001	Gender bias in screening recommendations	High	Medium	Bias testing on protected attributes, debiasing training data	Low
R-002	Discrimination against non-native language speakers	High	Medium	Multilingual evaluation, language-agnostic features	Medium
R-003	Over-reliance on AI recommendations by recruiters	Medium	High	Mandatory human review, confidence thresholds	Low
R-004	Inaccurate qualification matching for novel job roles	Medium	Medium	Fallback to keyword matching, human review flag	Low
R-005	Privacy breach via training data memorization	High	Low	Differential privacy in training, memorization audit	Low

Data Governance (Art. 10)

Training Data Requirements

Requirement	Implementation at Cerebrum AI Labs
Relevance and representativeness	Training data sourced from 50,000 job applications across 12 EU countries, balanced by gender, age, nationality
Bias examination	Statistical parity analysis on protected attributes (gender, age, ethnicity, disability) before and after training
Gap identification	Identified underrepresentation of applicants with disabilities; augmented with synthetic examples
Data quality	Automated data quality checks: completeness >95%, label accuracy >98% (human-verified sample)
Personal data processing	DPIA completed (DPIA-AI-2026-001); lawful basis: Art. 6(1)(f) legitimate interest; special categories removed

Conformity Assessment (Art. 43)

Assessment Procedure for Cerebrum AI Labs

System	Assessment Type	Basis
CV Screening AI	Internal conformity assessment (Art. 43(2)) + quality management system	Annex III, para. 4 — not biometric, not critical infrastructure
Credit Scoring AI	Internal conformity assessment (Art. 43(2))	Annex III, para. 5
Facial Verification AI	Third-party conformity assessment (Art. 43(1)) via notified body	Annex III, para. 1 — biometric identification

Internal Conformity Assessment Steps

1. Verify quality management system is established (Art. 17)
2. Prepare technical documentation per Annex IV
3. Conduct risk management assessment per Art. 9
4. Verify data governance per Art. 10
5. Verify transparency and information provision per Art. 13
6. Verify human oversight per Art. 14
7. Verify accuracy, robustness, and cybersecurity per Art. 15
8. Draw up EU Declaration of Conformity (Art. 47)
9. Affix CE marking (Art. 48)
10. Register in EU database (Art. 49)

Post-Market Monitoring (Art. 72)

Activity	Frequency	Owner
Performance metric monitoring	Continuous	ML Engineering
Bias drift detection	Weekly	Responsible AI team
Incident reporting	As needed (within 15 days for serious incidents per Art. 73)	DPO + Legal
User feedback collection	Continuous	Product team
Risk register update	Quarterly	Risk Management
Technical documentation update	On material change	ML Engineering + Legal

Key Legal References

• EU AI Act (Regulation 2024/1689) — Full text, entered into force 1 August 2024
• AI Act Art. 6 + Annex III — High-risk classification criteria
• AI Act Art. 9 — Risk management system requirements
• AI Act Art. 10 — Data and data governance requirements
• AI Act Art. 11 + Annex IV — Technical documentation requirements
• AI Act Art. 13 — Transparency and provision of information to deployers
• AI Act Art. 14 — Human oversight requirements
• AI Act Art. 43 — Conformity assessment procedures
• AI Act Art. 72 — Post-market monitoring obligations
• AI Act Art. 73 — Reporting of serious incidents
• GDPR Art. 22 — Automated individual decision-making (complementary to AI Act)
• EDPB-EDPS Joint Opinion 5/2021 on the AI Act Proposal — Data protection perspective on AI regulation