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naraburns  nihil supernum  1yr ago (text post)   31630 thread views

Culture War Roundup for the week of January 9, 2023

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AND doing that without including the race variable during training.

The effect size is very strong, so it's pretty easy to find features correlated with race that capture it. One public graph I've seen is fig 7 in this paper which shows a 10-20% racial gap in non-delinquency (i.e. at a FICO score of 600, 40% of blacks and 20% of asians go delinquent for the particular loan product in that dataset).

If you train on all variables except race and black people are ceteris paribus less likely to repay, won't that just create a distinct cluster unexplained by any visible variables? Sounds simple enough to then take an average of all such clusters.

You pretty much need to include it as a variable and then 'correct' for it -- otherwise any half-decent AI will just route around its absence, as you suggest.

If you just leave race out of the input set, most likely the system will find some proxy for race which works, and your model will still show "bias". (A very strict reading of "ceteris paribus" would mean you couldn't find such a proxy, but that's not what is meant). If you leave race out of the input set, and train it with the goal of being "unbiased", you can get an "unbiased" predictor (that is inferior at prediction), but it's a little too obvious.

It kind of sounds like the whole discriminating against black people thing was a bright idea the AI hit upon when it was instructed not to discriminate against poor people.

Sounds simple enough to then take an average of all such clusters

Why would you do that if you want to make money on the loans you give?

Because the algorithms are not being written by greedy bankers.

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