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?
"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?
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