The five Annex A controls for the data behind AI systems: data management processes, acquisition, data quality, provenance and data preparation.
Defining, documenting and implementing the data management processes used to develop and enhance AI systems: the umbrella data control that the acquisition, quality, provenance and preparation controls refine.
Determining and documenting how data for AI systems is acquired and selected: sources, rights and consent, selection criteria and the record an auditor checks for each dataset.
Defining and documenting data quality requirements and making sure data used to develop and operate AI systems meets them: quality dimensions, measurement, thresholds and remediation.
Defining and documenting a process for recording the provenance of data used in AI systems over the data and system life cycles: origin, lineage, transformations and integrity evidence.
Defining and documenting the criteria for choosing data preparation steps and the preparation methods used, such as cleaning, labeling, normalization and splitting, so results can be reproduced.
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