Does mitigating ML's impact disparity require treatment disparity?

Understand

Following related work in law and policy, two notions of disparity have come to shape the study of fairness in algorithmic decision-making.

• Algorithms exhibit treatment disparity if they formally treat members of protected subgroups differently; algorithms exhibit impact disparity when outcomes differ across subgroups, even if the correlation arises unintentionally.
• Naturally, we can achieve impact parity through purposeful treatment disparity.
• In one thread of technical work, papers aim to reconcile the two forms of parity proposing disparate learning processes (DLPs).