  This chapter is written with the Data Scientist or MLOps professional in mind
but can be used as a resource for policy makers, reformists, AI Ethicists,
sociologists, and others interested in finding methods that help reduce bias in
algorithms. I will take a deployment centered approach with the assumption that
the professionals reading this work have already read the amazing work on the
implications of algorithms on historically marginalized groups by Gebru,
Buolamwini, Benjamin and Shane to name a few. If you have not read those works,
I refer you to the "Important Reading for Ethical Model Building" list at the
end of this paper as it will help give you a framework on how to think about
Machine Learning models more holistically taking into account their effect on
marginalized people. In the Introduction to this chapter, I root the
significance of their work in real world examples of what happens when models
are deployed without transparent data collected for the training process and
are deployed without the practitioners paying special attention to what happens
to models that adapt to exploit gaps between their training environment and the
real world. The rest of this chapter builds on the work of the aforementioned
researchers and discusses the reality of models performing post production and
details ways ML practitioners can identify bias using tools during the MLOps
lifecycle to mitigate bias that may be introduced to models in the real world.
