AIF-C01 Guidelines for Responsible AI Practice Question
A hospital uses an AI system to prioritize patients for organ transplant based on predicted survival rates. The system was trained on historical data that includes socioeconomic factors. A review reveals that the system systematically assigns lower priority to patients from lower-income neighborhoods, even when medical urgency is similar. The hospital's ethics board demands an immediate remedy. The data science team is small and must act quickly. What should the hospital do to address this fairness issue most effectively?
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
✓
Retrain the model with only medically relevant features, after removing socioeconomic factors and correlated proxies
The best course is to retrain the model using only medically relevant features, removing socioeconomic factors and correlated proxies. This directly addresses the source of bias. Adding a penalty for low-income patients is artificial and may not reflect medical reality. Relying solely on human review delays the issue and introduces potential inconsistency. Using a different model without data changes may not eliminate bias.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Discontinue the AI system and have all prioritization done by a human committee
Why it's wrong here
Removing AI is a drastic step that reduces efficiency and may still have human bias.
- ✓
Retrain the model with only medically relevant features, after removing socioeconomic factors and correlated proxies
Why this is correct
Removing biased features addresses the root cause.
- ✗
Apply a re-weighting penalty to boost priority for low-income patients
Why it's wrong here
Artificial re-weighting may not be justified medically and could cause new biases.
- ✗
Use a different model type, such as a random forest instead of gradient boosting, on the same data
Why it's wrong here
Simply changing model type on biased data will not fix the bias.
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Written by Johnson Ajibi, MSc IT Security
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This AIF-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AIF-C01 exam.