  Despite widespread calls for transparent artificial intelligence systems, the
term is too overburdened with disparate meanings to express precise policy aims
or to orient concrete lines of research. Consequently, stakeholders often talk
past each other, with policymakers expressing vague demands and practitioners
devising solutions that may not address the underlying concerns. Part of why
this happens is that a clear ideal of AI transparency goes unsaid in this body
of work. We explicitly name such a north star -- transparency that is
user-centered, user-appropriate, and honest. We conduct a broad literature
survey, identifying many clusters of similar conceptions of transparency, tying
each back to our north star with analysis of how it furthers or hinders our
ideal AI transparency goals. We conclude with a discussion on common threads
across all the clusters, to provide clearer common language whereby
policymakers, stakeholders, and practitioners can communicate concrete demands
and deliver appropriate solutions. We hope for future work on AI transparency
that further advances confident, user-beneficial goals and provides clarity to
regulators and developers alike.
