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.

本文探讨了人机交互中系统设计与研究者如何允许系统确认其非人身份，研究了大量针对是否为机器人的表述与系统无法识别的内容，比较了不同分类器识别意图的效率和模型复杂度之间的权衡，提出了避免误导的虚假响应型对话系统的思路，并研究了已经投入使用的系统在确认非人身份方面的表现。最后，本文通过用户研究，总结了响应此意图所需的各个方面。