  Companies that try to address inequality in employment face a hiring paradox.
Failing to address workforce imbalance can result in legal sanctions and
scrutiny, but proactive measures to address these issues might result in the
same legal conflict. Recent run-ins of Microsoft and Wells Fargo with the Labor
Department's Office of Federal Contract Compliance Programs (OFCCP) are not
isolated and are likely to persist. To add to the confusion, existing
scholarship on Ricci v. DeStefano often deems solutions to this paradox
impossible. Circumventive practices such as the 4/5ths rule further illustrate
tensions between too little action and too much action.
  In this work, we give a powerful way to solve this hiring paradox that tracks
both legal and algorithmic challenges. We unpack the nuances of Ricci v.
DeStefano and extend the legal literature arguing that certain algorithmic
approaches to employment are allowed by introducing the legal practice of
banding to evaluate candidates. We thus show that a bias-aware technique can be
used to diagnose and mitigate "built-in" headwinds in the employment pipeline.
We use the machinery of partially ordered sets to handle the presence of
uncertainty in evaluations data. This approach allows us to move away from
treating "people as numbers" to treating people as individuals -- a property
that is sought after by Title VII in the context of employment.
