Evaluating Trust in the Context of Conversational Information Systems for new users of the Internet
This work tackles the problem of information access and trust for new internet users in non-English contexts, though it is incremental as it builds on existing research in conversational systems.
The study addressed the challenge of building trust in voice-based conversational information systems for new internet users with low English proficiency by developing four chatbot variants on Google Assistant that varied in gender, friendliness, and personalization. It found that users preferred the female, personalized bot overall, but individual ratings depended heavily on the bot's accuracy in understanding and responding to spoken queries.
Most online information sources are text-based and in Western Languages like English. However, many new and first time users of the Internet are in contexts with low English proficiency and are unable to access vital information online. Several researchers have focused on building conversational information systems over voice for this demographic, and also highlighted the importance of building trust towards the information source. In this work we develop four versions of a voice based chat-bot on the Google Assistant platform in which we vary the gender, friendliness and personalisation of the bot. We find that the users rank the female version of the bot with more personalisations over the others; however when rating the bots individually, the ratings depend on the ability of the bot to understand the users' spoken query and respond accurately.