  The aim of this paper is to facilitate nuanced discussion around research
norms and practices to mitigate the harmful impacts of advances in machine
learning (ML). We focus particularly on the use of ML to create "synthetic
media" (e.g. to generate or manipulate audio, video, images, and text), and the
question of what publication and release processes around such research might
look like, though many of the considerations discussed will apply to ML
research more broadly. We are not arguing for any specific approach on when or
how research should be distributed, but instead try to lay out some useful
tools, analogies, and options for thinking about these issues.
  We begin with some background on the idea that ML research might be misused
in harmful ways, and why advances in synthetic media, in particular, are
raising concerns. We then outline in more detail some of the different paths to
harm from ML research, before reviewing research risk mitigation strategies in
other fields and identifying components that seem most worth emulating in the
ML and synthetic media research communities. Next, we outline some important
dimensions of disagreement on these issues which risk polarizing conversations.
  Finally, we conclude with recommendations, suggesting that the machine
learning community might benefit from: working with subject matter experts to
increase understanding of the risk landscape and possible mitigation
strategies; building a community and norms around understanding the impacts of
ML research, e.g. through regular workshops at major conferences; and
establishing institutions and systems to support release practices that would
otherwise be onerous and error-prone.
