Technical Documentation

A logical schematic of the system's algorithmic structure and data flow. This must include the development methodology, design specifications, and the system's high-level architecture.

• Algorithmic Logic: A description of the model's design philosophy, including key architectural choices (e.g., transformer, CNN).
• Input Data Specifications: The precise format, nature, and source of the data the system ingests.
• Human-Machine Interface: A detailed description of the human oversight mechanisms and the controls available to the operator to override or interrupt the system.

An exhaustive account of how the model was built, validated, and tested. This section is critical for data governance and bias auditing.

• Training Data Provenance: The origin, selection criteria, and volume of the training, validation, and testing datasets.
• Pre-processing Steps: All data labeling, cleaning, and augmentation techniques applied.
• Data Governance Criteria: An explicit examination of the datasets for potential biases and an explanation of how they are relevant to the system's intended purpose.

The quantitative benchmarks that define the system's operational envelope. These metrics must be appropriate for the intended purpose and transparently disclosed.

• Key Performance Indicators: The specific, measurable metrics (e.g., F1 score, word error rate) used to evaluate the system.
• Confusion Matrices & Error Analysis: A breakdown of the types of errors the system makes and their potential consequences.
• Performance on Subgroups: A report on the system's accuracy across different demographic or operational cohorts to demonstrate fairness.

The documented output of the mandatory risk management system, linking identified hazards to specific mitigation measures. This section proves the system is safe for its intended use.

• Risk Register: A log of all reasonably foreseeable risks to health, safety, and fundamental rights.
• Mitigation Strategies: A direct mapping of each identified risk to a technical or organizational control, such as an adversarial robustness evaluation.
• Conformity Assessment Link: A clear reference to the relevant harmonized standards or technical specifications used to achieve the presumption of conformity.

A proactive, documented strategy for continuous surveillance of the system's real-world performance after deployment. This is not a static report but a living process.

• Monitoring Mechanisms: The technical tools used to collect performance data and user feedback from the live environment.
• Incident Thresholds: Pre-defined criteria that trigger a serious incident report to the market surveillance authority.
• Feedback Loops: The process by which collected data is fed back into the quality management system to trigger corrective actions or a new conformity assessment.