Understanding Circumstances for Desirable Proactive Behaviour of Voice Assistants: The Proactivity Dilemma

Zargham, Nima; Reicherts, Leon; Bonfert, Michael; Voelkel, Sarah Theres; Schoening, J.; Malaka, Rainer; Rogers, Yvonne

doi:10.1145/3543829.3543834

Cited by 45 publications

(17 citation statements)

References 39 publications

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“…This workshop is being organized by a team of experienced researchers who bring expertise from diverse fields aligned with our objectives. Collectively, the organizers have a track record of publishing on a wide range of topics, including conversational user interfaces [11,18,20,26,27,46,47], human-robot interaction [8,30], multi-modal communication [3,7], and privacy and security [5,6,15,24]. Integrating these disciplines shall facilitate collaboration among our trans-disciplinary participants and bridge the gap between these research areas.…”

Section: Organizersmentioning

confidence: 99%

CUI@CHI 2024: Building Trust in CUIs—From Design to Deployment

Desai,

Wei,

Sin

et al. 2024

Extended Abstracts of the CHI Conference on Human Factors in Computing Systems

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Conversational user interfaces (CUIs) have become an everyday technology for people the world over, as well as a booming area of research. Advances in voice synthesis and the emergence of chatbots powered by large language models (LLMs), notably ChatGPT, have pushed CUIs to the forefront of human-computer interaction (HCI) research and practice. Now that these technologies enable an elemental level of usability and user experience (UX), we must turn our attention to higher-order human factors: trust and reliance. In this workshop, we aim to bring together a multidisciplinary group of researchers and practitioners invested in the next phase of CUI design. Through keynotes, presentations, and breakout sessions, we will share our knowledge, identify cutting-edge resources, and fortify an international network of CUI scholars. In particular, we will engage with the complexity of trust and reliance as attitudes

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Section: Organizersmentioning

confidence: 99%

CUI@CHI 2024: Building Trust in CUIs—From Design to Deployment

Desai,

Wei,

Sin

et al. 2024

Extended Abstracts of the CHI Conference on Human Factors in Computing Systems

Self Cite

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“…All these aspects further raise privacy concerns. A significant body of research has highlighted the importance of user privacy and security [31,37,61,75,77]. Nevertheless, advances in the computing power of end devices could potentially reduce such concerns over time.…”

Section: Data Collectionmentioning

confidence: 99%

“…This means that some of the above data could potentially be processed on the users' devices without being transferred to the cloud. Nevertheless, users might not fully understand the (positive) implications of their data being processed on their device rather than in the cloud and many privacy concerns may remain (see also [77]).…”

Section: Data Collectionmentioning

confidence: 99%

“Funny How?” A Serious Look at Humor in Conversational Agents

Zargham

Avanesi

Reicherts

et al. 2023

Proceedings of the 5th International Conference on Conversational User Interfaces

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Conversational agents are rapidly advancing in terms of their capabilities and human likeness -both of which are intended to enhance the user experience and engagement. One human quality that can potentially increase trust and likeability is humor. However, what is considered humorous and what is not depends on many contextual and personal factors that are not only difficult for machines to detect, but even humans are still struggling to understand them. This makes training AI to be humorous highly challenging. But is this due only to the technical limitations? In this provocation paper, we discuss the hindrances to utilizing humor in commercial conversational agents and propose addressing this topic from a social and political perspective. CCS CONCEPTS• Human-centered computing → Natural language interfaces.

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“…Furthermore, a voice assistant delivering a warning is a form of proactive behavior initiated by the computer rather than the user [36,37]. In-vehicle voice assistants can provide personalized and adaptive suggestions, but users may ignore proactive behavior if it is inopportune, violates privacy, or distracts from driving [38][39][40]. However, emergencies are the most suitable context for proactive behavior that violates privacy [39].…”

Section: Introductionmentioning

confidence: 99%

Effectiveness and User Perception of an In-Vehicle Voice Warning for Hypoglycemia: Development and Feasibility Trial

Bérubé,

Lehmann,

Maritsch

et al. 2024

JMIR Hum Factors

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Background Hypoglycemia is a frequent and acute complication in type 1 diabetes mellitus (T1DM) and is associated with a higher risk of car mishaps. Currently, hypoglycemia can be detected and signaled through flash glucose monitoring or continuous glucose monitoring devices, which require manual and visual interaction, thereby removing the focus of attention from the driving task. Hypoglycemia causes a decrease in attention, thereby challenging the safety of using such devices behind the wheel. Here, we present an investigation of a hands-free technology—a voice warning that can potentially be delivered via an in-vehicle voice assistant. Objective This study aims to investigate the feasibility of an in-vehicle voice warning for hypoglycemia, evaluating both its effectiveness and user perception. Methods We designed a voice warning and evaluated it in 3 studies. In all studies, participants received a voice warning while driving. Study 0 (n=10) assessed the feasibility of using a voice warning with healthy participants driving in a simulator. Study 1 (n=18) assessed the voice warning in participants with T1DM. Study 2 (n=20) assessed the voice warning in participants with T1DM undergoing hypoglycemia while driving in a real car. We measured participants’ self-reported perception of the voice warning (with a user experience scale in study 0 and with acceptance, alliance, and trust scales in studies 1 and 2) and compliance behavior (whether they stopped the car and reaction time). In addition, we assessed technology affinity and collected the participants’ verbal feedback. Results Technology affinity was similar across studies and approximately 70% of the maximal value. Perception measure of the voice warning was approximately 62% to 78% in the simulated driving and 34% to 56% in real-world driving. Perception correlated with technology affinity on specific constructs (eg, Affinity for Technology Interaction score and intention to use, optimism and performance expectancy, behavioral intention, Session Alliance Inventory score, innovativeness and hedonic motivation, and negative correlations between discomfort and behavioral intention and discomfort and competence trust; all P<.05). Compliance was 100% in all studies, whereas reaction time was higher in study 1 (mean 23, SD 5.2 seconds) than in study 0 (mean 12.6, SD 5.7 seconds) and study 2 (mean 14.6, SD 4.3 seconds). Finally, verbal feedback showed that the participants preferred the voice warning to be less verbose and interactive. Conclusions This is the first study to investigate the feasibility of an in-vehicle voice warning for hypoglycemia. Drivers find such an implementation useful and effective in a simulated environment, but improvements are needed in the real-world driving context. This study is a kickoff for the use of in-vehicle voice assistants for digital health interventions.

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Understanding Circumstances for Desirable Proactive Behaviour of Voice Assistants: The Proactivity Dilemma

Abstract: Figure 1: An example storyboard used in the study. In this scenario (S4), the agent proactively approaches users based on their conversation.

Cited by 45 publications

References 39 publications

CUI@CHI 2024: Building Trust in CUIs—From Design to Deployment

CUI@CHI 2024: Building Trust in CUIs—From Design to Deployment

“Funny How?” A Serious Look at Humor in Conversational Agents

Effectiveness and User Perception of an In-Vehicle Voice Warning for Hypoglycemia: Development and Feasibility Trial

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