Verifying and documenting a valid legal basis: Which step most directly addresses the requirement to confirm
Confirming lawful rights means verifying and documenting a valid legal basis and permitted purpose for collecting and using each data source.
The question
Before a dataset is used to train an AI model, data-governance requirements must be met. Which step most directly addresses the requirement to confirm lawful rights to collect and use the data?
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- Normalizing the numeric feature columns to a common scale so that no single variable dominates the model's learned weighting during training.Plausible because feature scaling is standard preprocessing, but normalization is a technical quality step, not a determination of legal rights to use data.
- Splitting the dataset into training, validation and test partitions so the model's generalization can be measured on data it has not seen.Plausible because dataset splitting is essential for evaluation, but it concerns testing methodology rather than lawful authority to use the data.
- Augmenting the minority classes with synthetic examples so the training distribution is more balanced across the categories the model must predict.Plausible because class balancing can improve fairness and accuracy, but augmentation is a data-quality technique, not a check on legal rights.
- Verifying and documenting a valid legal basis and permitted purpose for collecting and using each data source in training. ✓Correct because confirming lawful rights means establishing and recording a valid legal basis and permitted purpose for each data source used.
The trap
Assuming technical data-preparation steps (scaling, splitting, augmentation) satisfy the lawful-rights requirement, which is a legal-basis and purpose determination. How to remember it
Confirming lawful rights means verifying and documenting a valid legal basis and permitted purpose for collecting and using each data source.
How many of these would you get right?
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