Data governance: Under the EU AI Act, which core requirement does this practice implement?
Ensuring dataset quality and examining data for bias implements the EU AI Act's data-governance requirement.
The question
A provider of a high-risk AI system must ensure its training, validation, and testing datasets meet quality criteria and are examined for possible biases. Under the EU AI Act, which core requirement does this practice implement?
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- Data governance, requiring that datasets meet quality criteria and are examined for possible bias before they are used. ✓Correct: dataset quality criteria and examination for bias are the essence of the data-governance requirement.
- Human oversight, requiring that a designated person be able to intervene in or stop the system's operation whenever necessary.Plausible but wrong: human oversight concerns operational control, not dataset quality.
- Incident reporting, requiring that serious malfunctions be reported to the relevant authority without undue delay.Plausible but wrong: incident reporting is post-market, not about dataset governance.
- Transparency to users, requiring that individuals be clearly told whenever they interact with the AI system.Plausible but wrong: user transparency concerns disclosure, not dataset quality and bias examination.
The trap
Mapping dataset quality and bias checks to transparency or oversight rather than the data-governance requirement. How to remember it
Ensuring dataset quality and examining data for bias implements the EU AI Act's data-governance requirement.
How many of these would you get right?
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Every answer, right and wrong, comes with its own explanation.