Following precedent in employment discrimination law, two notions of disparity are widely-discussed in papers on fairness and ML. Algorithms exhibit treatment disparity if they formally treat members of protected subgroups differently; algorithms exhibit impact disparity when outcomes differ across subgroups (even unintentionally). Naturally, we can achieve impact parity through purposeful treatment disparity. One line of papers aims to reconcile the two parities proposing disparate learning processes (DLPs). Here, the sensitive feature is used during training but a group-blind classifier is produced. In this paper, we show that: (i) when sensitive and (nominally) nonsensitive features are correlated, DLPs will indirectly implement treatment disparity, undermining the policy desiderata they are designed to address; (ii) when group membership is partly revealed by other features, DLPs induce within-class discrimination; and (iii) in general, DLPs provide suboptimal trade-offs between accuracy and impact parity. Experimental results on several real-world datasets highlight the practical consequences of applying DLPs.

This paper tackles a class of algorithms defined as Disparate Learning Processes (DLP) which use the sensitive feature while training and then make predictions without access at the sensitive feature. DLPs have appeared in multiple prior works, and the authors argue that DLPs do not necessarily guarantee treatment parity, which could then hurt impact parity. The theoretical analysis focuses on relating treatment disparity to utility and then optimal decision rules for various conditions. Most notably the per-group thresholding yields optimal rules to reduce the CV gap. As outlined in the beginning of section 4, the theoretical advantages of DLPs seems to optimality, rational ordering, and "no additional harm" to the protected group.

Following precedent in employment discrimination law, two notions of disparity are widely-discussed in papers on fairness and ML. Algorithms exhibit treatment disparity if they formally treat members of protected subgroups differently; algorithms exhibit impact disparity when outcomes differ across subgroups (even unintentionally). Naturally, we can achieve impact parity through purposeful treatment disparity. One line of papers aims to reconcile the two parities proposing disparate learning processes (DLPs). Here, the sensitive feature is used during training but a group-blind classifier is produced.