  Numerous Machine Learning (ML) bias-related failures in recent years have led
to scrutiny of how companies incorporate aspects of transparency and
accountability in their ML lifecycles. Companies have a responsibility to
monitor ML processes for bias and mitigate any bias detected, ensure business
product integrity, preserve customer loyalty, and protect brand image.
Challenges specific to industry ML projects can be broadly categorized into
principled documentation, human oversight, and need for mechanisms that enable
information reuse and improve cost efficiency. We highlight specific roadblocks
and propose conceptual solutions on a per-category basis for ML practitioners
and organizational subject matter experts. Our systematic approach tackles
these challenges by integrating mechanized and human-in-the-loop components in
bias detection, mitigation, and documentation of projects at various stages of
the ML lifecycle. To motivate the implementation of our system -- SIFT (System
to Integrate Fairness Transparently) -- we present its structural primitives
with an example real-world use case on how it can be used to identify potential
biases and determine appropriate mitigation strategies in a participatory
manner.
