  Humans are increasingly interacting with machines through language, sometimes
in contexts where the user may not know they are talking to a machine (like
over the phone or a text chatbot). We aim to understand how system designers
and researchers might allow their systems to confirm its non-human identity. We
collect over 2,500 phrasings related to the intent of ``Are you a robot?". This
is paired with over 2,500 adversarially selected utterances where only
confirming the system is non-human would be insufficient or disfluent. We
compare classifiers to recognize the intent and discuss the precision/recall
and model complexity tradeoffs. Such classifiers could be integrated into
dialog systems to avoid undesired deception. We then explore how both a
generative research model (Blender) as well as two deployed systems (Amazon
Alexa, Google Assistant) handle this intent, finding that systems often fail to
confirm their non-human identity. Finally, we try to understand what a good
response to the intent would be, and conduct a user study to compare the
important aspects when responding to this intent.
