Despite huge gains in performance in natural language understanding via large language models in recent years, voice assistants still often fail to meet user expectations. In this study, we conducted a mixed-methods analysis of how voice assistant failures affect users' trust in their voice assistants. To illustrate how users have experienced these failures, we contribute a crowdsourced dataset of 199 voice assistant failures, categorized across 12 failure sources. Relying on interview and survey data, we find that certain failures, such as those due to overcapturing users' input, derail user trust more than others. We additionally examine how failures impact users' willingness to rely on voice assistants for future tasks. Users often stop using their voice assistants for specific tasks that result in failures for a short period of time before resuming similar usage. We demonstrate the importance of low stakes tasks, such as playing music, towards building trust after failures.
@article{arxiv.2303.00164,
title = {A Mixed-Methods Approach to Understanding User Trust after Voice Assistant Failures},
author = {Amanda Baughan and Allison Mercurio and Ariel Liu and Xuezhi Wang and Jilin Chen and Xiao Ma},
journal= {arXiv preprint arXiv:2303.00164},
year = {2023}
}
Comments
16 pages, 3 figures. To appear in ACM CHI '23. for associated dataset file, see https://www.kaggle.com/datasets/googleai/voice-assistant-failures. Replacing the prior version with clean latex files, content remains unchanged