  The strive to make AI applications "safe" has led to the development of
safety-measures as the main or even sole normative requirement of their
permissible use. Similar can be attested to the latest version of chatbots,
such as chatGPT. In this view, if they are "safe", they are supposed to be
permissible to deploy. This approach, which we call "safety-normativity", is
rather limited in solving the emerging issues that chatGPT and other chatbots
have caused thus far. In answering this limitation, in this paper we argue for
limiting chatbots in the range of topics they can chat about according to the
normative concept of appropriateness. We argue that rather than looking for
"safety" in a chatbot's utterances to determine what they may and may not say,
we ought to assess those utterances according to three forms of
appropriateness: technical-discursive, social, and moral. We then spell out
what requirements for chatbots follow from these forms of appropriateness to
avoid the limits of previous accounts: positionality, acceptability, and value
alignment (PAVA). With these in mind, we may be able to determine what a
chatbot may and may not say. Lastly, one initial suggestion is to use challenge
sets, specifically designed for appropriateness, as a validation method.
