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Certain research strands can yield "forbidden knowledge". This term refers toknowledge that is considered too sensitive, dangerous or taboo to be producedor shared. Discourses about such publication restrictions are alreadyentrenched in scientific fields like IT security, synthetic biology or nuclearphysics research. This paper makes the case for transferring this discourse tomachine learning research. Some machine learning applications can very easilybe misused and unfold harmful consequences, for instance with regard togenerative video or text synthesis, personality analysis, behaviormanipulation, software vulnerability detection and the like. Up to now, themachine learning research community embraces the idea of open access. However,this is opposed to precautionary efforts to prevent the malicious use ofmachine learning applications. Information about or from such applications may,if improperly disclosed, cause harm to people, organizations or wholesocieties. Hence, the goal of this work is to outline norms that can help todecide whether and when the dissemination of such information should beprevented. It proposes review parameters for the machine learning community toestablish an ethical framework on how to deal with forbidden knowledge anddual-use applications.

Forbidden knowledge in machine learning -- Reflections on the limits of research and publication