TY - JOUR
T1 - Misalignment in Semantic User Model Elicitation via Conversational Agents
T2 - A Case Study in Navigation Support for Visually Impaired People
AU - Berka, Jakub
AU - Balata, Jan
AU - Jonker, Catholijn M.
AU - Mikovec, Zdenek
AU - van Riemsdijk, M. Birna
AU - Tielman, Myrthe
N1 - Funding Information:
This work is part of the research program CoreSAEP, with project number 639.022.416, which is financed by the Netherlands Organisation for Scientific Research (NWO). This research has been supported by projects Navigation of handicapped people funded by grant no. SGS19/178/OHK3/3T/13 and Research Center for Informatics (reg. n. CZ.02.1.01/0.0/0.0/16_019/0000765). We would like to thank our participants for their time and valuable input, without whom we would not have been able to conduct this study. We thank Pei-Yu Chen, a Ph.D. student at TU Delft, for her insights on user modeling and personalization, which have helped us position our work.
Publisher Copyright:
© 2022 The Author(s). Published with license by Taylor & Francis Group, LLC.
PY - 2022/12/14
Y1 - 2022/12/14
N2 - Disabled people can benefit greatly from assistive digital technologies. However, this increased human-machine symbiosis makes it important that systems are personalized and transparent to users. Existing work often uses data-oriented approaches. However, these approaches lack transparency and make it hard to influence the system’s behavior. In this paper, we use knowledge-based techniques for personalization, introducing the concept of Semantic User Models for representing the behavior, values and capabilities of users. To allow the system to construct such a user model, we investigate the use of a conversational agent which can elicit the relevant information from users through dialogue. A conversational interface is essential for our case study of navigation support for visually impaired people, but in general, has the potential to enhance transparency as users know what the system represents about them. For such a dialogue to be effective, it is crucial that the user understands what the conversational agent is asking, i.e., that misalignments that decrease the transparency are avoided or resolved. In this paper, we investigate whether we can use a conversational agent for Semantic User Model elicitation, which types of misalignments can occur in this process and how they are related, and how misalignments can be reduced. We investigate this in two (iterative) qualitative studies (n = 7 & n = 8) with visually impaired people in which a personalized user model for navigation support is elicited via a dialogue with a conversational agent. Our results show four hierarchically structured levels of human-agent misalignment. We identify several design solutions for reducing misalignments, which point to the need for restricting the generic user model to what is needed in the domain under consideration. With this research, we lay a foundation for conversational agents capable of eliciting Semantic User Models.
AB - Disabled people can benefit greatly from assistive digital technologies. However, this increased human-machine symbiosis makes it important that systems are personalized and transparent to users. Existing work often uses data-oriented approaches. However, these approaches lack transparency and make it hard to influence the system’s behavior. In this paper, we use knowledge-based techniques for personalization, introducing the concept of Semantic User Models for representing the behavior, values and capabilities of users. To allow the system to construct such a user model, we investigate the use of a conversational agent which can elicit the relevant information from users through dialogue. A conversational interface is essential for our case study of navigation support for visually impaired people, but in general, has the potential to enhance transparency as users know what the system represents about them. For such a dialogue to be effective, it is crucial that the user understands what the conversational agent is asking, i.e., that misalignments that decrease the transparency are avoided or resolved. In this paper, we investigate whether we can use a conversational agent for Semantic User Model elicitation, which types of misalignments can occur in this process and how they are related, and how misalignments can be reduced. We investigate this in two (iterative) qualitative studies (n = 7 & n = 8) with visually impaired people in which a personalized user model for navigation support is elicited via a dialogue with a conversational agent. Our results show four hierarchically structured levels of human-agent misalignment. We identify several design solutions for reducing misalignments, which point to the need for restricting the generic user model to what is needed in the domain under consideration. With this research, we lay a foundation for conversational agents capable of eliciting Semantic User Models.
UR - https://intimate-computing.net/my-papers/2022/tielman22IJHCI.pdf
U2 - 10.1080/10447318.2022.2059925
DO - 10.1080/10447318.2022.2059925
M3 - Article
SN - 1044-7318
VL - 38
SP - 1909
EP - 1925
JO - International journal of human-computer interaction
JF - International journal of human-computer interaction
IS - 18-20
ER -