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Question 25 of 38
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In practice, this would look like this: Using Age and Sex is legal → OK However, testing to ensure that their impact is in line with laws, regulations, and internal policies → required For example: Are the insurance premiums for 20-year-olds and 21-year-olds not too different? Are there any unintended biases in the data structure? Even if gender is not used directly, are proxy variables (such as car type or place of employment) acting as a substitute for gender? Fairness testing is necessary to identify these issues.
The correct answer is: ✅ C. Testing to ensure the model’s predictions do not discriminate based on age or gender Explanation: In an AI-based insurance pricing model, the most critical ethical concern is algorithmic fairness — ensuring that the model’s predictions do not discriminate against protected groups (e.g., by age, gender, race, or location). Testing for bias and discrimination ensures that: The model complies with ethical AI principles and anti-discrimination regulations. Premiums are determined based on legitimate risk factors rather than demographic proxies. The organization maintains transparency, accountability, and public trust in its AI systems. This type of testing is a key part of AI fairness and ethics auditing.