  We present the "Annotation and Benchmarking on Understanding and Transparency
of Machine Learning Lifecycles" (ABOUT ML) project as an initiative to
operationalize ML transparency and work towards a standard ML documentation
practice. We make the case for the project's relevance and effectiveness in
consolidating disparate efforts across a variety of stakeholders, as well as
bringing in the perspectives of currently missing voices that will be valuable
in shaping future conversations. We describe the details of the initiative and
the gaps we hope this project will help address.
