AI Associate Ethical Considerations of AI Practice Question
A government agency uses an AI system to allocate resources for public services such as healthcare and education. The system is designed to optimize overall efficiency based on historical usage data. After deployment, it becomes clear that underserved regions with less historical data receive significantly less funding than well-served regions. The agency's mission is to promote equity. The system's performance metrics show high efficiency, but community leaders protest the unfair distribution. What should the agency do?
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
✓
Redesign the system to include fairness constraints that ensure minimum resource levels for underserved regions.
Incorporating fairness constraints ensures equitable distribution while still using AI to optimize. Option A ignores the fairness issue. Option C is good but may not be sufficient if the model still biases against underrepresented areas. Option D reverts to a less efficient system.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Abandon the AI system and use a manual, rule-based allocation system.
Why it's wrong here
Manual systems can also be biased and are less efficient; improving the AI is preferable.
- ✓
Redesign the system to include fairness constraints that ensure minimum resource levels for underserved regions.
Why this is correct
Fairness constraints balance efficiency with equity, meeting both goals.
- ✗
Collect more historical data from underserved regions before making adjustments.
Why it's wrong here
While collecting more data is beneficial, it delays action and may not fully address the bias in the existing model.
- ✗
Continue using the system as is, since it maximizes efficiency.
Why it's wrong here
Efficiency without equity violates the agency's mission and ethical standards.
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