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From https://fairlearn.org/main/user_guide/datasets/boston_housing_data.html https://fairlearn.org/main/user_guide/datasets/boston_housin...: "Thus, any models

• loeg 4y ago
  House price prediction for prices in 1970s Boston, yes, where housing prices almost certainly reflected racist preferences. That seems like a (potentially) accurate model? ML models could also learn that the correlation between B and price is negative (i.e., that integration improves house prices). But the critics of the dataset all suggest that B and price are positively correlated.
• space_fountain 4y ago
  A confusion I sometimes have is where we should start pulling the string to unravel systematic racism. On some levels it can seem like if the purpose of this dataset was to analyze the impact of these variables on the prices humans (who were and are racist) are willing to pay than if B had predictive power (because people were racist), than excluding it in general seems wrong. On the other hand applying that across society is part of how we got redlining and in general a lot of the systematic part of systematic racism. Should we sort of handicap our models of human behavior to exclude race (and try to exclude proxies the model can find). I think regardless removing this model seems right since it's A a toy, B dated, and C liable to not be seen with the care it needs to be, but say if I'm trying to calculate who will be the next famous actor, is it wrong to include variables to allow the model to pick up on how shallow and say fat phobic society is (or just plain racist again)