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Session Overview
Session
Paper Session 05: Trust in Technology
Time:
Sunday, 31/Oct/2021:
2:00pm - 3:30pm

Session Chair: Jiangen He, University of Tennessee, Knoxville, USA
Location: Salon I, Lobby Level, Marriott


External Resource:
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Presentations
2:00pm - 2:30pm
ID: 113 / PS-05: 1
Long Papers
Confirmation 1: I/we agree if this paper/presentation is accepted, all authors/panelists listed as “presenters” will present during the Annual Meeting and will pay and register at least for the day of the presentation.
Confirmation 2: I/we further agree presenting authors/panelists who have not registered on or before the early bird registration deadline will be removed from the conference program, and their paper will be removed from the Proceedings.
Confirmation 3: I/we acknowledge that all session authors/presenters have read and agree to the ASIS&T Annual Meeting Policies found at https://www.asist.org/am21/submission-types-instructions/
Topics: Human Computer Interaction (HCI)
Keywords: Trusted AI, Intelligent Personal Assistant, Human Computer Interaction, Voice Interaction, Models

Why Do You Trust Siri? The Factors Affecting Trustworthiness of Intelligent Personal Assistant

Dan Wu, Ye-man Huang

Wuhan University, People's Republic of China

Trust greatly contributes to human-AI collaboration, however, human’s trust to IPA is hard to establish and lacks exploration. The purpose of this paper is to recognize the factors that affect the trustworthiness of IPA. 358 questionnaires were analyzed by PLS-SEM to construct the model, while thematic analysis was used to discover expectance of IPA. Chi-square tests and T-test were used to distinguish the difference between two user groups. Three factors that capability of system, personality of agent, and availability of interface have a significant impact on the trustworthiness of IPA. The capability of system is the most essential as the threshold with users’ plenty of expectations. Most users pay less attention to the availability of interface and the personality of agent has a great impact on the trustworthiness of IPA. The factors found enrich the trusted AI research and inspire insights of design of IPA.



2:30pm - 3:00pm
ID: 244 / PS-05: 2
Long Papers
Confirmation 1: I/we agree if this paper/presentation is accepted, all authors/panelists listed as “presenters” will present during the Annual Meeting and will pay and register at least for the day of the presentation.
Confirmation 2: I/we further agree presenting authors/panelists who have not registered on or before the early bird registration deadline will be removed from the conference program, and their paper will be removed from the Proceedings.
Confirmation 3: I/we acknowledge that all session authors/presenters have read and agree to the ASIS&T Annual Meeting Policies found at https://www.asist.org/am21/submission-types-instructions/
Topics: Library and Information Science
Keywords: COVID-19, coronavirus, Google Autocomplete, conspiracy theory, social contagion

“COVID19 is_”: The Perpetuation of Coronavirus Conspiracy Theories via Google Autocomplete

Daniel Houli, Marie Radford, Vivek Singh

Rutgers, the State University of New Jersey, USA

As the COVID-19 pandemic spread in 2020, uncertainty surrounding its origins and nature led to widespread conspiracy-related theories (CRT). Use of technological platforms enabled the rapid and exponential spread of COVID-19 CRT. This study applies social contagion theory to examine how Google Autocomplete (GA) perpetuates COVID-19 CRT. An in-house software program, Autocomplete Search Logging Tool (ASLT) captured a snapshot of GA COVID-19 related searches early in the pandemic (from March to May 2020) across 76 randomly-selected countries to gain insight into search behaviors thought to reflect beliefs globally. The authors identified 15 keywords relating to COVID-19 CRT predictions. Findings show that the searches across different countries received varying degrees of GA COVID-19 CRT predictions. This investigation is among the first to apply social contagion theory to autocomplete applications and can be used in future research to explain and perhaps mitigate the spread of CRT.



3:00pm - 3:30pm
ID: 266 / PS-05: 3
Long Papers
Confirmation 1: I/we agree if this paper/presentation is accepted, all authors/panelists listed as “presenters” will present during the Annual Meeting and will pay and register at least for the day of the presentation.
Confirmation 2: I/we further agree presenting authors/panelists who have not registered on or before the early bird registration deadline will be removed from the conference program, and their paper will be removed from the Proceedings.
Confirmation 3: I/we acknowledge that all session authors/presenters have read and agree to the ASIS&T Annual Meeting Policies found at https://www.asist.org/am21/submission-types-instructions/
Topics: Library and Information Science
Keywords: Voice Digital Assistants, Interface Mirroring, Voice Switching Behavior, Inclusive Design

Hey There! What Do You Look Like? User Voice Switching and Interface Mirroring in Voice-Enabled Digital Assistants (VDAs)

Dania Bilal, Jessica Barfield

University of Tennessee, Knoxville, USA

We investigated user voice switching behavior (VSB) in voice-enabled digital assistants (VDAs), focusing on the importance of and preference for the voice accents, genders, and age to match with those of the users. We incorporated images of ten people with diverse races, ethnicities, age, genders, and religions to embody the voice interfaces (EVIs). In an online survey, we collected demographic, background, and VDA usage data. The sample consisted of 214 participants recruited through Amazon Mechanical Turk (http://mturk.com). The participants were selected based on owning a VDA (e.g., Alexa Home) or owning a device (e.g., smartphone, tablet, or computer), and setting the device on English as the default language. The age of the participants ranged from 18-35 years. Findings revealed that, regardless of age, the majority of the participants switched the voice interface and for various reasons. Further, participants placed importance on voice matching with their gender, accent, and age. Participants ranked the young White female, Asian female, and Black female EVIs as the most preferred for voice switching and interactions. We coin the concept, Interface mirroring, which should help designers to create more diverse and inclusive EVIs, ensuring fairness and equality in the design of VDAs.