AI-900 Question #174: Real Exam Question with Answer & Explanation

Question

You are developing a system to predict the prices of insurance for drivers in the United Kingdom. You need to minimize bias in the system. What should you do?

Options

• ARemove information about protected characteristics from the data before sampling.
• BTake a training sample that is representative of the population in the United Kingdom.
• CCreate a training dataset that uses data from global insurers.
• DTake a completely random training sample.

Explanation

To minimize bias in an insurance pricing system for UK drivers, incorporating data from global insurers can introduce a broader perspective beyond local historical biases.

Common mistakes.

• A. Removing protected characteristics might prevent direct discrimination but doesn't necessarily eliminate bias if other features serve as proxies or if underlying historical biases are still present in the remaining data.
• B. Taking a training sample that is representative of the UK population is important, but if the entire UK dataset inherently contains systemic biases, merely sampling it representatively will perpetuate those biases rather than minimize them.
• D. A completely random training sample might still reflect existing biases in the underlying data distribution and may not ensure fair representation across all relevant subgroups, especially in complex populations.

Concept tested. Data Diversity for Bias Mitigation

Reference. https://learn.microsoft.com/en-us/azure/cloud-adoption-framework/innovate/best-practices/responsible-ai-fairness

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