AI Associate Ethical Considerations of AI Practice Question
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.
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.
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.
- ✗
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.
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