Ethics for AI Writing

Implicit in any rhetorical interaction-between humans or between humans and machines-are ethical codes that shape the rhetorical context, the social situation in which communication happens and also the engine that drives communicative interaction. Such implicit codes are usually invisible to AI writing systems because the social factors shaping communication (the why and how of language, not the what) are not usually explicitly evident in databases the systems use to produce discourse. Can AI writing systems learn to learn rhetorical context, particularly the implicit codes for communication ethics? We see evidence that some systems do address issues of rhetorical context, at least in rudimentary ways. But we critique the information transfer communication model supporting many AI writing systems, arguing for a social context model that accounts for rhetorical context-what is, in a sense, "not there" in the data corpus but that is critical for the production of meaningful, significant, and ethical communication. We offer two ethical principles to guide design of AI writing systems: transparency about machine presence and critical data awareness, a methodological reflexivity about rhetorical context and omissions in the data that need to be provided by a human agent or accounted for in machine learning.