Making the case for audience design in conversational AI: Rapport expectations and language ideologies in a task-oriented chatbot

• URL: http://arxiv.org/abs/2206.10694v1
• Date: Tue, 21 Jun 2022 19:21:30 GMT
• Title: Making the case for audience design in conversational AI: Rapport expectations and language ideologies in a task-oriented chatbot
• Authors: Doris Dippold
• Abstract summary: This paper argues that insights into users' language ideologies and their rapport expectations can be used to inform the audience design of the bot's language and interaction patterns. I will define audience design for conversational AI and discuss how user analyses of interactions and socio-linguistically informed theoretical approaches can be used to support audience design.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Chatbots are more and more prevalent in commercial and science contexts. They help customers complain about a product or service or support them to find the best travel deals. Other bots provide mental health support or help book medical appointments. This paper argues that insights into users' language ideologies and their rapport expectations can be used to inform the audience design of the bot's language and interaction patterns and ensure equitable access to the services provided by bots. The argument is underpinned by three kinds of data: simulated user interactions with a chatbot facilitating health appointment bookings, users' introspective comments on their interactions and users' qualitative survey comments post engagement with the booking bot. In closing, I will define audience design for conversational AI and discuss how user-centred analyses of chatbot interactions and sociolinguistically informed theoretical approaches, such as rapport management, can be used to support audience design.

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