A documented data-governance assessment: Which evidence best addresses high-risk data-governance uncertainty
High-risk data governance concerns documented data suitability, including relevance, quality, representativeness, errors, and bias.
The question
In an EU media company, assume relevant high-risk AI Act duties apply to a system ranking content for editorial promotion. Training data combine historical audience logs and licensed archives. Which evidence best addresses high-risk data-governance uncertainty before approval?
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- A documented data-governance assessment. ✓A substantive data-governance assessment can address source selection, relevance, quality, representativeness, errors, and potential bias for the ranking purpose.
- An archive-license inventory naming data owners.Licensing records address permissions and ownership, not whether the data are relevant, representative, accurate, or assessed for bias.
- Validation accuracy on a held-out audience sample.Validation accuracy informs performance but does not document source characteristics, known errors, representativeness, or bias examination.
- A senior editor’s approval of every promoted item.Editorial approval may provide downstream oversight, but it does not establish that training and validation data were appropriately governed.
The trap
Separate permission to use data from evidence that the data are suitable, representative, accurate, and assessed for bias. How to remember it
High-risk data governance concerns documented data suitability, including relevance, quality, representativeness, errors, and bias.
How many of these would you get right?
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