Data and Data Governance for High-Risk AI Systems

Providers of high-risk AI systems must ensure training, validation, and testing datasets meet strict quality criteria, including relevance, representativeness, and completeness. They must implement robust data governance practices covering design choices, data collection processes, preparation operations, and bias detection. Providers may process special categories of personal data to correct biases but must adhere to strict safeguards. Data management must align with the AI system's intended use and address potential gaps or errors.