@inproceedings{cercas-curry-rieser-2018-metoo,
    title = "{\#}{M}e{T}oo {A}lexa: How Conversational Systems Respond to Sexual Harassment",
    author = "Cercas Curry, Amanda  and
      Rieser, Verena",
    editor = "Alfano, Mark  and
      Hovy, Dirk  and
      Mitchell, Margaret  and
      Strube, Michael",
    booktitle = "Proceedings of the Second {ACL} Workshop on Ethics in Natural Language Processing",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/iwcs-25-ingestion/W18-0802/",
    doi = "10.18653/v1/W18-0802",
    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."
}