@InProceedings{cercascurry-rieser:2018:W18-08,
  author    = {Cercas Curry, Amanda  and  Rieser, Verena},
  title     = {\#MeToo Alexa: How Conversational Systems Respond to Sexual Harassment},
  booktitle = {Proceedings of the Second ACL Workshop on Ethics in Natural Language Processing},
  month     = {June},
  year      = {2018},
  address   = {New Orleans, Louisiana, USA},
  publisher = {Association for Computational Linguistics},
  pages     = {7--14},
  abstract  = {Conversational AI systems, such as Amazon’s Alexa, are rapidly developing from purely transactional systems to social chatbots, which can respond to a wide variety of user requests. In this article, we establish how current state-of-the-art conversational systems react to inappropriate requests, such as bullying and sexual harassment on the part of the user, by collecting and analysing the novel \#MeTooAlexa corpus. Our results show that commercial systems mainly avoid answering, while rule-based chatbots show a variety of behaviours and often deflect. Data-driven systems, on the other hand, are often non-coherent, but also run the risk of being interpreted as flirtatious and sometimes react with counter-aggression. This includes our own system, trained on “clean” data, which suggests that inappropriate system behaviour is not caused by data bias.},
  url       = {http://www.aclweb.org/anthology/W18-0802}
}