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

• URL: http://arxiv.org/abs/2106.02692v1
• Date: Fri, 4 Jun 2021 20:04:33 GMT
• Title: The R-U-A-Robot Dataset: Helping Avoid Chatbot Deception by Detecting User Questions About Human or Non-Human Identity
• Authors: David Gros, Yu Li, Zhou Yu
• Abstract summary: We aim to understand how system designers 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?" We compare classifiers to recognize the intent and discuss the precision/recall and model complexity tradeoffs.
• Score: 41.43519695929595
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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