Data Governance Criteria

Training, validation, and testing datasets must be relevant to the system's intended purpose and representative of the population or phenomenon being modeled.

• Geographic diversity: Data must cover all intended deployment regions
• Demographic balance: Avoids under-representation of protected groups
• Contextual alignment: Data reflects the specific operational environment

Failure to ensure representativeness is a primary source of unfair bias and a direct violation of the Act's essential requirements.

A systematic, documented process must examine datasets for errors, biases, and inaccuracies before model training begins.

• Statistical bias detection: Identifying skewed distributions in labels or features
• Measurement error analysis: Assessing sensor noise or human annotation inconsistencies
• Historical bias audit: Recognizing and mitigating biases embedded in legacy data

This examination is not a one-time event but a continuous obligation tied to the risk management system.

Datasets must be complete and possess adequate coverage of all scenarios, edge cases, and failure modes relevant to the system's intended purpose.

• Missing data handling: Documented protocols for imputation or exclusion
• Edge case enumeration: Explicit coverage of rare but high-risk scenarios
• Temporal coverage: Data spanning relevant time periods to avoid concept drift

Incomplete datasets that fail to capture critical safety scenarios render the conformity assessment invalid.

The origin, chain of custody, and transformation history of every data point must be traceable and documented.

• Source identification: Clear attribution of data origin (synthetic, collected, purchased)
• Transformation logging: Immutable record of all preprocessing and augmentation steps
• Consent verification: Proof of lawful basis for personal data processing under GDPR

Provenance documentation is critical for IP compliance and defending against claims of unauthorized data usage.

Quantifiable quality metrics must be defined, measured, and enforced throughout the data lifecycle.

• Accuracy rates: Minimum acceptable annotation or sensor precision
• Completeness ratios: Thresholds for missing value tolerance
• Consistency scores: Cross-validation of conflicting data sources

These metrics form the basis of the quality management system and must be continuously monitored during post-market surveillance.

When processing special category data (race, health, biometrics) for bias monitoring, strict technical and organizational measures are mandatory.

• Purpose limitation: Sensitive data used only for bias detection, never for other purposes
• Access controls: Role-based restrictions with audit logging
• Pseudonymization: Stripping direct identifiers wherever possible

These safeguards are a derogation under GDPR Article 9, permitted solely for ensuring fairness in high-risk AI systems.

The systematic process of identifying and mitigating statistical bias in training datasets that could lead to discriminatory outcomes. Under the EU AI Act, providers must examine datasets for prohibited biases related to sensitive attributes like race, gender, and age.

• Disparate Impact Testing: Measuring whether model outcomes disproportionately harm protected groups
• Fairness Metrics: Including demographic parity, equalized odds, and individual fairness
• Pre-processing Techniques: Reweighting and resampling to balance representation before training

The comprehensive framework for managing training data quality, provenance, lineage, and copyright compliance throughout the machine learning lifecycle. This discipline ensures datasets meet the stringent requirements of Article 10 of the EU AI Act.

• Data Lineage Tracking: Documenting the complete journey of data from source to training
• Provenance Verification: Establishing the origin and ownership rights of all data points
• Copyright Compliance: Ensuring training data respects intellectual property laws and licensing terms

The specialized protocols for managing artificially generated training datasets used to supplement or replace real-world data. While synthetic data can address privacy concerns and data scarcity, it introduces unique governance challenges.

• Quality Control: Verifying synthetic data maintains statistical fidelity to real distributions
• Privacy Risk Assessment: Ensuring generated samples cannot be reverse-engineered to reveal original records
• Provenance Documentation: Clearly labeling synthetic data and its generation methodology

The comprehensive dossier required under the EU AI Act that demonstrates a high-risk system's compliance, including detailed information on dataset specifications and governance procedures. This documentation must be maintained throughout the system's lifecycle.

• Dataset Description: Size, composition, labeling methodology, and collection protocols
• Pre-processing Steps: All cleaning, filtering, and augmentation applied to raw data
• Bias Assessment Results: Documented findings from fairness and representativeness examinations

The technical infrastructure enabling the fulfillment of privacy requests related to AI training data, including access rights, rectification, and erasure. Under GDPR and the AI Act, individuals have rights over personal data used in model development.

• Right to Explanation: Providing meaningful information about the logic of automated decisions
• Consent Management: Tracking and honoring data subject consent for AI training purposes
• Automated Erasure: Technical mechanisms to remove individual data from training pipelines

Technical measures enforcing data minimization and preventing the repurposing of data beyond its original collection intent. The EU AI Act requires that training data be relevant and appropriate for the system's intended purpose.

• Access Controls: Restricting dataset usage to authorized training workflows
• Data Tagging: Metadata systems that encode permitted use cases and retention limits
• Audit Mechanisms: Monitoring systems that detect unauthorized data repurposing attempts