- A
Defend the model based on its actuarial accuracy and historical claims data.
Why wrong: Actuarial accuracy does not justify unfair treatment if it perpetuates historical discrimination.
- B
Incorporate a fairness constraint that requires similar premiums for similar risk profiles regardless of ZIP code.
This ensures fairness while preserving the model's ability to differentiate based on actual risk.
- C
Cap premium increases in low-income neighborhoods at a fixed percentage.
Why wrong: Capping does not address the model's internal bias and may lead to underpricing of risk.
- D
Remove ZIP code from the model inputs entirely.
Why wrong: ZIP code may be a proxy for other risk factors; removal may reduce model accuracy and still not eliminate bias if other features correlate.
AI Associate Ethical Considerations of AI Practice Question
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.
An insurance company uses an AI model to set auto insurance premiums. The model uses factors including driving history, age, and ZIP code. A regulator finds that premiums in certain low-income neighborhoods are significantly higher than in affluent neighborhoods with similar risk profiles. The company's actuaries argue that the model is actuarially sound because it accurately predicts claims based on historical data. The company wants to comply with ethical guidelines and avoid legal action. Which action should they take?
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
Incorporate a fairness constraint that requires similar premiums for similar risk profiles regardless of ZIP code.
Option B is correct because introducing a fairness check ensures that similar risk levels result in similar premiums across neighborhoods, addressing ethical concerns without discarding valid risk factors. Option A ignores the issue. Option C removes a potentially relevant factor, but may reduce accuracy. Option D is a band-aid that doesn't fix underlying bias.
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.
- ✗
Defend the model based on its actuarial accuracy and historical claims data.
Why it's wrong here
Actuarial accuracy does not justify unfair treatment if it perpetuates historical discrimination.
- ✓
Incorporate a fairness constraint that requires similar premiums for similar risk profiles regardless of ZIP code.
Why this is correct
This ensures fairness while preserving the model's ability to differentiate based on actual risk.
Related concept
OSPF neighbours must agree on key parameters.
- ✗
Cap premium increases in low-income neighborhoods at a fixed percentage.
Why it's wrong here
Capping does not address the model's internal bias and may lead to underpricing of risk.
- ✗
Remove ZIP code from the model inputs entirely.
Why it's wrong here
ZIP code may be a proxy for other risk factors; removal may reduce model accuracy and still not eliminate bias if other features correlate.
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 network engineer at a university connects two campus buildings via a fibre link. Both routers run OSPF, but no adjacency forms — even though both routers can ping each other. The engineer finds one router is in area 0 and the other in area 1. OSPF adjacency requires matching area numbers, hello/dead timers, and network type. IP reachability alone is not enough.
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: Incorporate a fairness constraint that requires similar premiums for similar risk profiles regardless of ZIP code. — Option B is correct because introducing a fairness check ensures that similar risk levels result in similar premiums across neighborhoods, addressing ethical concerns without discarding valid risk factors. Option A ignores the issue. Option C removes a potentially relevant factor, but may reduce accuracy. Option D is a band-aid that doesn't fix underlying bias.
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.
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Last reviewed: Jun 23, 2026
This AI Associate practice question is part of Courseiva's free Salesforce 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 AI Associate exam.
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