- A
Abandon the AI system and use a manual, rule-based allocation system.
Why wrong: Manual systems can also be biased and are less efficient; improving the AI is preferable.
- B
Redesign the system to include fairness constraints that ensure minimum resource levels for underserved regions.
Fairness constraints balance efficiency with equity, meeting both goals.
- C
Collect more historical data from underserved regions before making adjustments.
Why wrong: While collecting more data is beneficial, it delays action and may not fully address the bias in the existing model.
- D
Continue using the system as is, since it maximizes efficiency.
Why wrong: Efficiency without equity violates the agency's mission and ethical standards.
Question 863 of 1,000
Using Fairness Constraints to Ensure Equitable AI Resource Allocation
This AI Associate practice question tests your understanding of ethical considerations of ai. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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.
Option B is correct because 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.
Key principle: OSPF neighbour adjacency depends on matching area, hello/dead timers, network type, and authentication — IP reachability alone is not enough.
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.
Related concept
OSPF neighbours must agree on key parameters.
- ✗
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.
Common exam traps
Common exam trap: OSPF can fail even when IP connectivity looks correct
OSPF neighbour formation depends on matching areas, timers, network type, authentication and passive-interface behaviour. Do not choose an answer only because the devices can ping.
Detailed technical explanation
How to think about this question
OSPF questions usually test the details that control adjacency and route selection. Read the neighbour state, area, router ID and interface configuration before deciding what is wrong.
KKey Concepts to Remember
- OSPF neighbours must agree on key parameters.
- Router ID selection can affect neighbour relationships and LSDB output.
- OSPF cost influences the preferred path.
- A route can appear in OSPF information but not become the installed route.
TExam Day Tips
- Check area mismatch first when OSPF adjacency fails.
- Review passive interfaces when a network is advertised but no neighbour forms.
- Use show ip ospf neighbor and show ip route clues carefully.
Key takeaway
OSPF neighbour adjacency depends on matching area, hello/dead timers, network type, and authentication — IP reachability alone is not enough.
Real-world example
How this comes up in practice
A practitioner preparing for the AI Associate exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. OSPF neighbour adjacency depends on matching area, hello/dead timers, network type, and authentication — IP reachability alone is not enough. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
What to study next
Got this wrong? Here's your next step.
Review OSPF neighbour requirements — matching area type, hello and dead timers, network type, stub flags, and authentication. Study show ip ospf neighbor states (INIT, 2-WAY, FULL). Then practise related AI Associate OSPF questions on adjacency and route selection.
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FAQ
Questions learners often ask
What does this AI Associate question test?
Ethical Considerations of AI — This question tests Ethical Considerations of AI — OSPF neighbours must agree on key parameters..
What is the correct answer to this question?
The correct answer is: Redesign the system to include fairness constraints that ensure minimum resource levels for underserved regions. — Option B is correct because 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.
What should I do if I get this AI Associate question wrong?
Review OSPF neighbour requirements — matching area type, hello and dead timers, network type, stub flags, and authentication. Study show ip ospf neighbor states (INIT, 2-WAY, FULL). Then practise related AI Associate OSPF questions on adjacency and route selection.
What is the key concept behind this question?
OSPF neighbours must agree on key parameters.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Same concept, more angles
3 more ways this is tested on AI Associate
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A company deployed an AI model for lead scoring. After several months, they notice that leads from certain geographic regions consistently receive higher scores than leads from other regions with similar demographic profiles. The company wants to ensure ethical AI usage. What should they do first?
medium- A.Adjust the scoring thresholds for each region to equalize scores.
- ✓ B.Retrain the model using a more diverse and balanced training dataset.
- C.Ignore the discrepancy since the model overall accuracy is high.
- D.Remove the geographic region feature from the model completely.
Why B: Option B is correct because retraining with more diverse data addresses potential bias at the source. Option A adjusts thresholds without fixing root cause. Option C ignores the discrepancy, which does not address the bias. Option D removes a feature that may be relevant but could still leak bias through correlated features.
Variation 2. An AI system for hiring is deployed. After six months, the HR team notices that the model's recommendations closely mimic past human hires, which were biased. The team wants to correct this. What should be their first step?
hard- A.Shut down the AI system entirely
- ✓ B.Implement continuous monitoring and a feedback loop to detect and mitigate bias
- C.Retrain the model with the same historical data but with more features
- D.Make the model's decision process fully transparent to all candidates
Why B: Option B is correct: Continuous monitoring and feedback loops can detect and correct drift or bias. Option A is wrong because removing the model does not solve underlying bias. Option C is wrong because past data already contains bias. Option D is wrong because complete transparency does not automatically correct bias.
Variation 3. A news aggregator app uses an AI algorithm to personalize the news feed for each user. The algorithm selects articles based on past clicks and reading time. Recently, a study reveals that the algorithm disproportionately shows sensational and polarizing news to users from certain political orientations, while showing more neutral content to others. The company's user engagement metrics have increased, but journalists express concern about reinforcing echo chambers and misinformation. The company wants to uphold ethical standards while keeping users engaged. What should they do?
medium- ✓ A.Modify the algorithm to include diversity and reliability scores for news sources, promoting a balanced feed.
- B.Allow users to manually select the types of news they want to see.
- C.Show the same generic news feed to all users.
- D.Continue with the current algorithm since it increases engagement.
Why A: Option A is correct because modifying the algorithm to include diversity and reliability scores for news sources directly addresses the ethical concerns of echo chambers and misinformation while maintaining user engagement. This approach balances personalization with ethical responsibility by promoting a balanced feed. Option B shifts the burden to users and may not effectively counter algorithmic biases. Option C eliminates personalization entirely, likely reducing engagement. Option D ignores ethical issues and risks amplifying harmful content.
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Last reviewed: Jun 23, 2026
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