#MeToo Alexa: How Conversational Systems Respond to Sexual Harassment

Abstract

Conversational AI systems 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 #MeToo 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 noncoherent, 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.Recently, widespread sexual harassment allegations following the #MeToo 3 campaign have propelled the issue of what constitutes harassment and how to respond to it to the media's attention.Given that most virtual assistants have femalesounding names and voices, it begs the question of how often these systems are harassed and how they respond to harassment (Silvervarg et al., 2012).

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