  The documentation practice for machine-learned (ML) models often falls short
of established practices for traditional software, which impedes model
accountability and inadvertently abets inappropriate or misuse of models.
Recently, model cards, a proposal for model documentation, have attracted
notable attention, but their impact on the actual practice is unclear. In this
work, we systematically study the model documentation in the field and
investigate how to encourage more responsible and accountable documentation
practice. Our analysis of publicly available model cards reveals a substantial
gap between the proposal and the practice. We then design a tool named DocML
aiming to (1) nudge the data scientists to comply with the model cards proposal
during the model development, especially the sections related to ethics, and
(2) assess and manage the documentation quality. A lab study reveals the
benefit of our tool towards long-term documentation quality and accountability.
