  Specialized documentation techniques have been developed to communicate key
facts about machine-learning (ML) systems and the datasets and models they rely
on. Techniques such as Datasheets, FactSheets, and Model Cards have taken a
mainly descriptive approach, providing various details about the system
components. While the above information is essential for product developers and
external experts to assess whether the ML system meets their requirements,
other stakeholders might find it less actionable. In particular, ML engineers
need guidance on how to mitigate potential shortcomings in order to fix bugs or
improve the system's performance. We survey approaches that aim to provide such
guidance in a prescriptive way. We further propose a preliminary approach,
called Method Cards, which aims to increase the transparency and
reproducibility of ML systems by providing prescriptive documentation of
commonly-used ML methods and techniques. We showcase our proposal with an
example in small object detection, and demonstrate how Method Cards can
communicate key considerations for model developers. We further highlight
avenues for improving the user experience of ML engineers based on Method
Cards.
