  Saliency methods are a common class of machine learning interpretability
techniques that calculate how important each input feature is to a model's
output. We find that, with the rapid pace of development, users struggle to
stay informed of the strengths and limitations of new methods and, thus, choose
methods for unprincipled reasons (e.g., popularity). Moreover, despite a
corresponding rise in evaluation metrics, existing approaches assume universal
desiderata for saliency methods (e.g., faithfulness) that do not account for
diverse user needs. In response, we introduce saliency cards: structured
documentation of how saliency methods operate and their performance across a
battery of evaluative metrics. Through a review of 25 saliency method papers
and 33 method evaluations, we identify 10 attributes that users should account
for when choosing a method. We group these attributes into three categories
that span the process of computing and interpreting saliency: methodology, or
how the saliency is calculated; sensitivity, or the relationship between the
saliency and the underlying model and data; and, perceptibility, or how an end
user ultimately interprets the result. By collating this information, saliency
cards allow users to more holistically assess and compare the implications of
different methods. Through nine semi-structured interviews with users from
various backgrounds, including researchers, radiologists, and computational
biologists, we find that saliency cards provide a detailed vocabulary for
discussing individual methods and allow for a more systematic selection of
task-appropriate methods. Moreover, with saliency cards, we are able to analyze
the research landscape in a more structured fashion to identify opportunities
for new methods and evaluation metrics for unmet user needs.
