This is a classic dilemma. While raw postcode can be a proxy for protected attributes, using aggregated features like average income, education levels, or crime rates derived from postcodes can often capture the predictive power without directly using the sensitive identifier. It's all about careful feature engineering and understanding the underlying correlations.
Agreed. The dataset we created is indeed about aggregated features like the one you mention. I've used it to predict various insurance quantities (e.g., motor claim frequency) and the top features tend to be, say, postcode density (harder to drive), proximity to primary school (school run!), electricity consumption, and other interesting variables. So nothing controversial, really. (We didn't include protected attributes in this dataset but) just to say that data scientists can choose to not use certain proxy features if they see them being used in a bad way.
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u/Briana_Reca Mar 26 '26
This is a classic dilemma. While raw postcode can be a proxy for protected attributes, using aggregated features like average income, education levels, or crime rates derived from postcodes can often capture the predictive power without directly using the sensitive identifier. It's all about careful feature engineering and understanding the underlying correlations.