@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://aclanthology.org/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.",
}
Markdown (Informal)
[#MeToo Alexa: How Conversational Systems Respond to Sexual Harassment](https://aclanthology.org/W18-0802) (Cercas Curry & Rieser, EthNLP 2018)
ACL