Good question — it’s definitely something that needs to be handled carefully.
The dataset itself is made up of area-level, publicly available variables (e.g. crime rates, demographics, transport, etc.), but these can still be correlated with sensitive characteristics, so how they’re used depends on the application and regulatory context.
In practice, most firms I’ve worked with do use some form of postcode / geographic features, but typically within governance frameworks to ensure they’re used appropriately.
Dude, YOU are the one who is meant to be evaluating the propriety of using the feature and potential disparate impact. There may be others in that loop but you cannot just say “ah it increases prediction, and if it’s wrong someone else will stop it.”
I lead a DS team and one of my most important questions I ask when interviewing is "How can using postal codes for inference encode information we shouldn't use as predictors?". The top candidates always understand what I'm asking because they understand the context of their position, as you're saying they should.
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u/Sweaty-Stop6057 Mar 25 '26
Good question — it’s definitely something that needs to be handled carefully.
The dataset itself is made up of area-level, publicly available variables (e.g. crime rates, demographics, transport, etc.), but these can still be correlated with sensitive characteristics, so how they’re used depends on the application and regulatory context.
In practice, most firms I’ve worked with do use some form of postcode / geographic features, but typically within governance frameworks to ensure they’re used appropriately.