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?

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.

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.

Leveraging data from global insurers can help minimize bias by providing a more diverse and comprehensive dataset, potentially diluting biases inherent in a purely local dataset by exposing the model to a wider range of driving behaviors, risk factors, and pricing strategies from different markets.

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.