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Humans are increasingly interacting with machines through language, sometimesin contexts where the user may not know they are talking to a machine (likeover the phone or a text chatbot). We aim to understand how system designersand researchers might allow their systems to confirm its non-human identity. Wecollect over 2,500 phrasings related to the intent of ``Are you a robot?". Thisis paired with over 2,500 adversarially selected utterances where onlyconfirming the system is non-human would be insufficient or disfluent. Wecompare classifiers to recognize the intent and discuss the precision/recalland model complexity tradeoffs. Such classifiers could be integrated intodialog systems to avoid undesired deception. We then explore how both agenerative research model (Blender) as well as two deployed systems (AmazonAlexa, Google Assistant) handle this intent, finding that systems often fail toconfirm their non-human identity. Finally, we try to understand what a goodresponse to the intent would be, and conduct a user study to compare theimportant aspects when responding to this intent.

The R-U-A-Robot Dataset: Helping Avoid Chatbot Deception by Detecting User Questions About Human or Non-Human Identity