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Exploring the Effects of Interactive Dialogue in Improving User Control for Explainable Online Symptom Checkers

Authors:

S. Shyam SundarAuthors Info & Claims

CHI EA '22: CHI Conference on Human Factors in Computing Systems Extended Abstracts

Article No.: 417, Pages 1 - 7

https://doi.org/10.1145/3491101.3519668

Published: 28 April 2022 Publication History

Abstract

There has been a major push to improve the transparency of online symptom checkers (OSCs) by providing more explanations to users about their functioning and conclusions. However, not all users will want explanations about all aspects of these systems. A more user-centered approach is necessary for personalizing user experience of explanations. With this in mind, we designed and tested an interactive dialogue interface to afford user control to receive only those explanations that they would like to read. A user study (N = 152) with a text-based chatbot for assessing anxiety levels and presented explanations to participants in one of the three forms–an interactive dialogue providing choice for viewing different components of the explanations, a static disclosure of all explanations, and a control condition with no explanations whatsoever. We found that participants varied in the kinds of information they wanted to learn. The interactive delivery of explanations led to higher levels of perceived transparency and affective trust in the system. Furthermore, both subjective and objective understanding of the mechanism used for assessing anxiety was higher for participants in the interactive dialogue condition. We discuss theoretical and practical implications of imbuing interactivity for enhancing the effectiveness of explainable systems.

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Cited By

Chen CLiao MSundar S(2024)When to Explain? Exploring the Effects of Explanation Timing on User Perceptions and Trust in AI systemsProceedings of the Second International Symposium on Trustworthy Autonomous Systems10.1145/3686038.3686066(1-17)Online publication date: 16-Sep-2024
https://dl.acm.org/doi/10.1145/3686038.3686066
Sun YJang EMa FWang T(2024)Generative AI in the Wild: Prospects, Challenges, and StrategiesProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642160(1-16)Online publication date: 11-May-2024
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cover image ACM Conferences

CHI EA '22: Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems

April 2022

3066 pages

ISBN:9781450391566

DOI:10.1145/3491101

Editors:
Simone Barbosa
PUC-Rio, Brazil
,
Cliff Lampe
University of Michigan, USA
,
Caroline Appert
Université Paris-Saclay, France
,
David A. Shamma
Toyota Research Institute, USA

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Published: 28 April 2022

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Cited By

Chen CLiao MSundar S(2024)When to Explain? Exploring the Effects of Explanation Timing on User Perceptions and Trust in AI systemsProceedings of the Second International Symposium on Trustworthy Autonomous Systems10.1145/3686038.3686066(1-17)Online publication date: 16-Sep-2024
https://dl.acm.org/doi/10.1145/3686038.3686066
Sun YJang EMa FWang T(2024)Generative AI in the Wild: Prospects, Challenges, and StrategiesProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642160(1-16)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642160
Chen CKang JSajjadi PSundar S(2024)Preventing users from going down rabbit holes of extreme video contentInternational Journal of Human-Computer Studies10.1016/j.ijhcs.2024.103303190:COnline publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1016/j.ijhcs.2024.103303
You YTsai CLi YMa FHeron CGui X(2023)Beyond Self-diagnosis: How a Chatbot-based Symptom Checker Should RespondACM Transactions on Computer-Human Interaction10.1145/358995930:4(1-44)Online publication date: 11-Sep-2023
https://dl.acm.org/doi/10.1145/3589959
Sun YSong QGui XMa FWang T(2023)AutoML in The Wild: Obstacles, Workarounds, and ExpectationsProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3581082(1-15)Online publication date: 19-Apr-2023
https://dl.acm.org/doi/10.1145/3544548.3581082
Chen CSundar S(2023)Is this AI trained on Credible Data? The Effects of Labeling Quality and Performance Bias on User TrustProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3580805(1-11)Online publication date: 19-Apr-2023
https://dl.acm.org/doi/10.1145/3544548.3580805
Liu YYan WHu BLin ZSong Y(2023)Chatbots or Humans? Effects of Agent Identity and Information Sensitivity on Users’ Privacy Management and Behavioral Intentions: A Comparative Experimental Study between China and the United StatesInternational Journal of Human–Computer Interaction10.1080/10447318.2023.223897440:19(5632-5647)Online publication date: 14-Aug-2023
https://doi.org/10.1080/10447318.2023.2238974
Bunde EEisenhardt DSonntag DProfitlich HMeske C(2023)Giving DIAnA More TIME – Guidance for the Design of XAI-Based Medical Decision Support SystemsDesign Science Research for a New Society: Society 5.010.1007/978-3-031-32808-4_7(107-122)Online publication date: 31-May-2023
https://dl.acm.org/doi/10.1007/978-3-031-32808-4_7

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