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
Active Learning reduces manual labeling but does not address labeler bias.
- ✗
Public workforce with no qualification
Why it's wrong here
Unqualified public workers may introduce inconsistent or biased labels.
- ✓
Private workforce of domain experts
Why this is correct
Domain experts provide high-quality, consistent labels, reducing bias.
- ✗
Vendor managed workforce
Why it's wrong here
AWS Ground Truth does not have a built-in vendor workforce; vendors are third-party.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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