Use of offensive language in human-artificial intelligence chatbot interaction: The effects of ethical ideology, social competence, and perceived humanlikeness

By Namkee Park, Kyungeun Jang, Seonggyeol Cho, Jinyoung Choi. 2021

63 citations recorded by the source corpus.

Abstract This study examined the factors that affect artificial intelligence (AI) chatbot users’ use of profanity and offensive words, employing the concepts of ethical ideology, social competence, and perceived human-likeness of chatbot. The study also looked into users’ liking of chatbots’ responses to the users’ utterance of profanity and offensive words. Using a national survey (N = 645), the study found that users’ idealism orientation was a significant factor in explaining use of such offensive language. In addition, users with high idealism revealed liking of chatbots’ active intervention, whereas those with high relativism displayed liking of chatbots’ reactive responses. Moreover, users’ perceived human-likeness of chatbot increased their likelihood of using offensive words targeting dislikable acquaintances, racial/ethnic groups, and political parties. These findings are expected to fill the gap between the current use of AI chatbots and the lack of empirical studies examining language use.

Subfields: Artificial Intelligence, Social Psychology, Natural Language Processing

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Citations in the corpus

BlenderBot 3: a deployed conversational agent that continually learns to responsibly engage (2022, background): “The challenge, however, is that organic users may not be invested enough to want to provide adequate feedback, and some may be adversarial (Park et al., 2021) as in the case of Microsoft’s Tay (Davis, 2016).”