AIF-C01 Guidelines for Responsible AI Practice Question
A media company uses Amazon Transcribe for automatic speech recognition. They discover the model has higher error rates for non-native English speakers. Which Responsible AI principle are they failing to uphold?
⚠ Common exam trap
AWS often tests the distinction between Fairness and Robustness, where candidates mistakenly attribute performance disparities to a lack of robustness rather than recognizing it as a fairness issue stemming from biased training data.
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
✓
Fairness
The model's higher error rates for non-native English speakers indicate a bias in the training data or model design that leads to disparate performance across demographic groups. This directly violates the Fairness principle of Responsible AI, which requires that AI systems treat all groups equitably and do not amplify existing societal biases. Amazon Transcribe's underlying acoustic and language models may have been trained predominantly on native English speech, causing systematic underperformance for non-native accents.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Fairness
Why this is correct
Fairness requires that a system perform equitably across demographic groups. Higher error rates for non-native English speakers show the transcription model delivers unequal accuracy for a particular group, breaching that principle rather than, say, transparency or privacy.
- ✗
Explainability
Why it's wrong here
Explainability concerns understanding how a model reaches its outputs, not unequal accuracy across speaker groups. It is tempting because Responsible AI frameworks list explainability alongside fairness, so any model concern can appear to fit. Here the disparity in error rates for non-native speakers is a bias and fairness failure.
- ✗
Robustness
Why it's wrong here
Robustness covers resilience to adversarial input, noise or distribution shift, not systematic accuracy gaps between demographic groups. It is tempting because degraded performance on certain inputs sounds like fragility, but the stem describes unequal error rates for non-native speakers, which is a fairness and bias problem.
- ✗
Privacy
Why it's wrong here
Privacy governs handling of personal data — consent, retention and access — not transcription accuracy differences between speaker groups. It is tempting because speech data is sensitive personal information, so privacy feels relevant. The stem's higher error rates for non-native English speakers indicate bias, a fairness failure.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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