Technical Documentation File

A complete blueprint of the AI system's computational logic and structural design. This section must detail the system architecture, including data flows, algorithmic logic, and the interaction between hardware and software components. It provides the foundational context for the conformity assessment.

• Key Elements: System topology diagrams, data pipeline schematics, and model architecture specifications.
• Purpose: To allow regulators to understand the system's operational boundaries and identify potential failure points.
• Linkage: Directly supports the Intended Purpose Declaration by mapping the technical implementation to the stated use case.

A detailed account of the iterative risk management process conducted throughout the AI system's lifecycle. This section must document known and foreseeable risks, their severity, and the specific mitigation measures implemented.

• Process: Includes risk identification, estimation, evaluation, and the adoption of risk control measures.
• Output: A Residual Risk Disclosure statement that transparently communicates any remaining risks that could not be eliminated.
• Integration: This documentation is the primary input for the Algorithmic Impact Assessment and must be continuously updated as part of Post-Market Monitoring.

A comprehensive log of the datasets used for training, validation, and testing the AI model. This component provides evidence of data quality, integrity, and legal compliance.

• Critical Inclusions: Training Data Provenance Records detailing data origin, selection criteria, and pre-processing steps.
• Bias Examination: Documentation of how datasets were examined for potential biases that could lead to discriminatory outcomes.
• Compliance: Demonstrates adherence to AI Data Governance rules, including copyright compliance and the handling of personal data under GDPR.

The structured disclosure of the AI model's capabilities, limitations, and performance benchmarks. This section is formalized through a Model Card Submission and must provide clear, interpretable metrics.

• Metrics: Accuracy, robustness, and error rates measured against predefined benchmarks and test datasets.
• Limitations: Explicit documentation of the model's known failure modes and the conditions under which performance degrades.
• Explainability: A summary of the Model Explainability Techniques employed to interpret the model's outputs, ensuring alignment with the right to explanation.

A detailed specification of the Human Oversight Mechanisms built into the system to prevent or minimize risks. This section defines the interface between the AI and its human operator.

• Control Measures: Describes whether the system uses human-in-the-loop, human-on-the-loop, or human-in-command approaches.
• Interface Design: Documentation of the user interface tools that enable operators to interpret outputs, override decisions, or stop the system entirely.
• Competency: Outlines the required training and competence level for the human operators assigned to oversee the system.

Evidence of the system's technical resilience against errors, adversarial manipulation, and unauthorized access. This section validates the system's AI Cybersecurity Hardening and operational reliability.

• Robustness: Documentation of Adversarial Robustness Evaluation results, demonstrating resistance to evasion attacks and data poisoning.
• Accuracy: Specification of the system's accuracy rate and the metrics used to measure it.
• Security: A description of security measures including input sanitization, output moderation, and defenses against model inversion, ensuring the integrity of the Automated Decision Logging trail.