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Comment by wyager | Hacker News Reader

This seems to be the (extremely unreasonable) standard held by parties who are either statistically ignorant or statistically malicious.

A vastly more reasonable standard, but easy to confuse without stats background, is e.g. http://ttic.uchicago.edu/~nati/Publications/HardtPriceSrebro...

Attempting to censor nodes in a classifier’s Bayesian network will strictly result in some combination of A) the classifier routing around the damage and B) the classifier producing worse results. Either way, we’ve accomplished nothing. The linked paper, on the other hand, still allows the classifier to be accurate without giving incorrect results across protected categories.

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The paper was beyond my level of stats-comprehension, so I couldn't figure out the answer to this question while looking it over. How does their methodology get around the 'redundant encodings' problem?

I am going to assume by 'redundant encoding' you mean a model that takes some non-racial feature like- living in an urban area- and uses that to predict something that is very different across races- say whether or not your loan is approved.

"Definition 2.1 (Equalized odds). We say that a predictor Yhat satisfies equalized odds with respect to protected attribute A and outcome Y , if Yhat and A are independent conditional on Y ."

This is from page 3. Yhat is the model trained on A (protected class) T (training outcome).

Do you see how if this definition holds there can be absolutely no redundant encoding?

Yes that helped quite a bit. Looking over that section, I thought this summarized it quite well: "For the outcome y = 1, the constraint requires that Yhat has equal true positive rates across the two demographics A = 0 and A = 1. For y = 0, the constraint equalizes false positive rates."

Fantastic!

I thought more about your question (at least what I thought it was) and it wouldn't necessarily prevent redundant encoding but it would sort of restrict how 'damaging' such an encoding could be (if that makes sense).

This whole field is very new but very exciting and very troubling.

Like- what is fairness really? Its an intersection of philosophy/ethics and very UN-intuitive mathematics..there are many open questions

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