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?
"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?