AIF-C01 Guidelines for Responsible AI Practice Question
A company uses Amazon SageMaker Ground Truth to label a dataset for a binary classifier. To reduce labeling bias, which workforce configuration is most appropriate?
⚠ Common exam trap
A common mistake is assuming that automated or crowd-sourced labeling is always less biased or more efficient. In AWS SageMaker Ground Truth, for specialized tasks, a private workforce of domain experts is critical to avoid introducing systematic labeling errors that degrade model fairness.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Private workforce of domain experts
A private workforce of domain experts ensures that labeling is performed by individuals with deep knowledge of the data domain, which directly reduces labeling bias. Domain experts are less likely to misinterpret ambiguous data points and can apply consistent, informed judgment, thereby minimizing systematic errors that could skew the binary classifier's training data.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Automatic labeling with Active Learning
Why it's wrong here
Automatic labelling with active learning has the model label the most confident examples itself, so its own biases are reinforced rather than challenged. It is tempting because it cuts cost and human effort on large, already-balanced datasets, but reducing labelling bias requires independent human annotators.
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Public workforce with no qualification
Why it's wrong here
An unqualified public workforce lets any worker label tasks, producing inconsistent, low-quality annotations that entrench bias rather than reduce it. It is tempting because it is the cheapest, fastest way to clear a large backlog, but bias mitigation requires qualified annotators with demonstrated accuracy on the task.
- ✓
Private workforce of domain experts
Why this is correct
A private workforce of domain experts satisfies the bias-reduction constraint by restricting labelling to vetted specialists with relevant subject knowledge, rather than anonymous crowd workers who may apply inconsistent or culturally skewed judgements. For binary classification, this yields more reliable ground-truth labels, though it costs more and scales slowly.
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Vendor managed workforce
Why it's wrong here
A vendor-managed workforce supplies one external team whose shared conventions and domain assumptions create systematic, correlated errors across the whole dataset. It is tempting because vendors scale quickly and handle sensitive data under contract, but bias reduction needs diverse, independent annotator groups rather than a single homogeneous pool.
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