research-article

Designing a Conversational Agent for Sexual Assault Survivors: Defining Burden of Self-Disclosure and Envisioning Survivor-Centered Solutions

Authors:

Joonhwan LeeAuthors Info & Claims

CHI '21: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems

Article No.: 634, Pages 1 - 17

https://doi.org/10.1145/3411764.3445133

Published: 07 May 2021 Publication History

Abstract

Sexual assault survivors hesitate to disclose their stories to others and even avoid case-reporting because of psychological, social, and cultural reasons. Thus, conversational agents (CAs) have gained much attention as a potential counselor because CAs’ characteristics (e.g., anonymity) could mitigate various difficulties of human-human interaction (HHI). Despite the potentials, it is difficult to design a CA for survivors because various aspects should be considered. Especially, with traditional HCI approaches only (e.g., need-finding and usability tests), designers could easily miss psychological and subjective burdens that survivors feel toward a new system. Hence, while envisioning a burden-free CA for survivors, we agilely designed and implemented an initial prototype CA (NamuBot) with professionals (the police and counselors). We then conducted a qualitative user study to identify and compare burdens caused by the CA vs. humans. Lastly, we codesigned design features that could reduce the CA-bound burdens with 36 participants (19 survivors and 17 professionals). Notably, our findings showed that 17 survivors preferred reporting their case to NamuBot over humans, expressing far less burden. Although CAs could also place burdens on survivors, the burdens could be alleviated by the features that the survivors and professionals designed. Finally, we present design implications and strategies to develop burden-mitigating CAs for survivors.

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Cited By

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Wang CTseng Y(2024)“I Don't Want to be Pitied by a Bot”: Understand How to Design Chatbots to Support People Being Ghosted on Dating ApplicationsExtended Abstracts of the CHI Conference on Human Factors in Computing Systems10.1145/3613905.3650957(1-8)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613905.3650957
Park H(2024)Re-examining User Burden in Human-AI Interaction: Focusing on a Domain-Specific ApproachExtended Abstracts of the CHI Conference on Human Factors in Computing Systems10.1145/3613905.3638186(1-4)Online publication date: 11-May-2024
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Index Terms

Designing a Conversational Agent for Sexual Assault Survivors: Defining Burden of Self-Disclosure and Envisioning Survivor-Centered Solutions
1. Human-centered computing
  1. Human computer interaction (HCI)
  2. Interaction design

Index terms have been assigned to the content through auto-classification.

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cover image ACM Conferences

CHI '21: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems

May 2021

10862 pages

ISBN:9781450380966

DOI:10.1145/3411764

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Yoshifumi Kitamura
Tohoku University, Japan
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University of New South Wales, Australia
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University of California Santa Cruz, USA
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Takeo Igarashi
The University of Tokyo, Japan
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University of Copenhagen, Denmark
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Microsoft Research, USA

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Cited By

Mühl LStecker LHerter ESzczuka JWischnewski MKrämer N(2024)Integrating AI in Psychotherapy: An Investigation of Trust in Voicebot TherapistsProceedings of the 13th Nordic Conference on Human-Computer Interaction10.1145/3679318.3685353(1-9)Online publication date: 13-Oct-2024
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Wang CTseng Y(2024)“I Don't Want to be Pitied by a Bot”: Understand How to Design Chatbots to Support People Being Ghosted on Dating ApplicationsExtended Abstracts of the CHI Conference on Human Factors in Computing Systems10.1145/3613905.3650957(1-8)Online publication date: 11-May-2024
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