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

Reducing malicious use of synthetic media research: Considerations and potential release practices for machine learning