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I was mostly referring to this bit from the article: “Notice how the model that has the lowest mean squared error is also the model that causes the most bias be
by sbpayne 6y ago
I was mostly referring to this bit from the article: “Notice how the model that has the lowest mean squared error is also the model that causes the most bias between groups for higher skill levels. This increase in bias cannot be blamed merely on the data. It’s the choice of the model that increases this bias which is the responsibility of the algorithm designer.”
My reading of the plots is that the difference between the groups seems to grow beyond what is present in the data. Do you not think this is amplified?
As for conditioning on a biased variable; this post uses a contrived example for sure. But the same thing happens with variables that correlate with the “naughty” variable (perhaps author should have explicitly showed this to drive the point?)
Removing all variables that correlate with “naughty variables” is a pretty difficult task itself without having something to detect the correlation and tell you. At least that’s been my experience.
- mr_toad 6y ago> This increase in bias cannot be blamed merely on the data. This is debatable in this case. The grouped model might be a better fit to the data: it indicates a higher bias in salaries at higher skill levels. It sounds completely plausible that the gender bias for unskilled workers is less than for high skilled workers. Models can be under-fit as well as over fit. Just using a dummy variable for gender might not properly capture interactions like this. But of course it’s always possible that this is just an artefact of the training data.