Data Governance for AI is the mandatory, systematic framework under the EU AI Act through which providers of high-risk AI systems manage, document, and ensure the quality, relevance, and integrity of all data used throughout the AI system's lifecycle, from initial training and validation through to post-market monitoring.

This requirement recognizes that the performance, fairness, and safety of an AI system are intrinsically linked to the quality of its underlying data. It extends traditional data governance beyond security and access control to specifically address AI risks, such as embedded biases and non-representative datasets. The framework requires documented policies and procedures to actively curate, evaluate, and monitor data, ensuring it is fit for purpose and does not perpetuate discrimination or lead to inaccurate outcomes. Effective data governance is the foundational first step in complying with broader requirements for fairness, robustness, and transparency.

Implementing robust AI data governance involves several key processes:

Data Curation & Provenance: Establishing processes to select, clean, and document training, validation, and testing datasets, including clear records of sources, collection methods, and any pre-processing applied.

Bias & Representativeness Assessment: Proactively analyzing datasets for under-representation of specific groups and for historical or societal biases that could lead the AI system to produce discriminatory outputs.

Data Quality Management: Defining and enforcing measurable standards for data relevance, completeness, correctness, and currency (up-to-dateness) specific to the AI system's intended purpose.

Lifecycle Management: Governing not only the initial training data but also the data used for ongoing validation, testing, and model updates, ensuring consistency and quality throughout the system's operational life.

Regulatory Context: Article 10 of the EU AI Act imposes explicit data governance requirements for high-risk AI systems, mandating that training, validation, and testing datasets be "relevant, representative, free of errors and complete." This provision directly links data quality to the mitigation of risks to health, safety, and fundamental rights, creating a legal "duty of care" over datasets.

Risk Mitigation Foundation: Comprehensive data governance is the most effective upstream control for preventing downstream harms. By systematically addressing data flaws before they are learned by the model, organizations can reduce the need for costly corrective measures post-deployment, build more trustworthy systems, and create a defensible audit trail demonstrating proactive compliance.

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