Details
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 3rd Conference on Conversational User Interfaces, CUI 2021 |
| Publisher | Association for Computing Machinery (ACM) |
| Number of pages | 5 |
| ISBN (print) | 9781450389983 |
| Publication status | Published - 27 Jul 2021 |
| Externally published | Yes |
| Event | 3rd Conference on Conversational User Interfaces, CUI 2021 - Virtual, Online, Spain Duration: 27 Jul 2021 → 29 Jul 2021 |
Abstract
Many questions of public interest do not have a single answer but come with a set of choices, each of which with its pros and cons. An "objective"information system can help explore the underlying argument space, and, if equipped with a conversational interface, it can create the experience of lively discussions resembling those from our daily lives. However, users will (subconsciously) extend the provided information by assumptions that adhere to their cognitive biases. In this regard, note that biases do not arise only from the underlying data or the employed algorithms, but also from the way the information is presented - especially in audio-only channels. Our paper brings attention to bias-related challenges of conversational interfaces for argument search systems. We identify research questions that address these challenges, and we propose ideas and methods to tackle them.
Keywords
- argument search, cognitive bias, conversational search, intelligent assistants, selection bias
ASJC Scopus subject areas
- Computer Science(all)
- Software
- Computer Science(all)
- Human-Computer Interaction
- Computer Science(all)
- Computer Vision and Pattern Recognition
- Computer Science(all)
- Computer Networks and Communications
Cite this
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Proceedings of the 3rd Conference on Conversational User Interfaces, CUI 2021. Association for Computing Machinery (ACM), 2021. 20.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - The Meant, the Said, and the Understood
T2 - 3rd Conference on Conversational User Interfaces, CUI 2021
AU - Kiesel, Johannes
AU - Spina, Damiano
AU - Wachsmuth, Henning
AU - Stein, Benno
N1 - Funding Information: Damiano Spina is the recipient of an Australian Research Council DECRA Research Fellowship (DE200100064).
PY - 2021/7/27
Y1 - 2021/7/27
N2 - Many questions of public interest do not have a single answer but come with a set of choices, each of which with its pros and cons. An "objective"information system can help explore the underlying argument space, and, if equipped with a conversational interface, it can create the experience of lively discussions resembling those from our daily lives. However, users will (subconsciously) extend the provided information by assumptions that adhere to their cognitive biases. In this regard, note that biases do not arise only from the underlying data or the employed algorithms, but also from the way the information is presented - especially in audio-only channels. Our paper brings attention to bias-related challenges of conversational interfaces for argument search systems. We identify research questions that address these challenges, and we propose ideas and methods to tackle them.
AB - Many questions of public interest do not have a single answer but come with a set of choices, each of which with its pros and cons. An "objective"information system can help explore the underlying argument space, and, if equipped with a conversational interface, it can create the experience of lively discussions resembling those from our daily lives. However, users will (subconsciously) extend the provided information by assumptions that adhere to their cognitive biases. In this regard, note that biases do not arise only from the underlying data or the employed algorithms, but also from the way the information is presented - especially in audio-only channels. Our paper brings attention to bias-related challenges of conversational interfaces for argument search systems. We identify research questions that address these challenges, and we propose ideas and methods to tackle them.
KW - argument search
KW - cognitive bias
KW - conversational search
KW - intelligent assistants
KW - selection bias
UR - http://www.scopus.com/inward/record.url?scp=85112313824&partnerID=8YFLogxK
U2 - 10.1145/3469595.3469615
DO - 10.1145/3469595.3469615
M3 - Conference contribution
AN - SCOPUS:85112313824
SN - 9781450389983
BT - Proceedings of the 3rd Conference on Conversational User Interfaces, CUI 2021
PB - Association for Computing Machinery (ACM)
Y2 - 27 July 2021 through 29 July 2021
ER -