24,019 research outputs found

    Troubling Vulnerability: Designing with LGBT Young People's Ambivalence Towards Hate Crime Reporting

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    HCI is increasingly working with ?vulnerable? people yet there is a danger that the label of vulnerability can alienate and stigmatize the people such work aims to support. We report our study investigating the application of interaction design to increase rates of hate crime reporting amongst Lesbian, Gay, Bisexual and Transgender young people. During design-led workshops participants expressed ambivalence towards reporting. While recognizing their exposure to hate crime they simultaneously rejected ascription as victim as implied in the act of reporting. We used visual communication design to depict the young people?s ambivalent identities and contribute insights on how these fail and succeed to account for the intersectional, fluid and emergent nature of LGBT identities through the design research process. We argue that by producing ambiguous designed texts, alongside conventional qualitative data, we ?trouble? our design research narratives as a tactic to disrupt static and reductive understandings of vulnerability within HCI

    The Effects of Health Care Chatbot Personas With Different Social Roles on the Client-Chatbot Bond and Usage Intentions: Development of a Design Codebook and Web-Based Study

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    Background The working alliance refers to an important relationship quality between health professionals and clients that robustly links to treatment success. Recent research shows that clients can develop an affective bond with chatbots. However, few research studies have investigated whether this perceived relationship is affected by the social roles of differing closeness a chatbot can impersonate and by allowing users to choose the social role of a chatbot. Objective This study aimed at understanding how the social role of a chatbot can be expressed using a set of interpersonal closeness cues and examining how these social roles affect clients’ experiences and the development of an affective bond with the chatbot, depending on clients’ characteristics (ie, age and gender) and whether they can freely choose a chatbot’s social role. Methods Informed by the social role theory and the social response theory, we developed a design codebook for chatbots with different social roles along an interpersonal closeness continuum. Based on this codebook, we manipulated a fictitious health care chatbot to impersonate one of four distinct social roles common in health care settings—institution, expert, peer, and dialogical self—and examined effects on perceived affective bond and usage intentions in a web-based lab study. The study included a total of 251 participants, whose mean age was 41.15 (SD 13.87) years; 57.0% (143/251) of the participants were female. Participants were either randomly assigned to one of the chatbot conditions (no choice: n=202, 80.5%) or could freely choose to interact with one of these chatbot personas (free choice: n=49, 19.5%). Separate multivariate analyses of variance were performed to analyze differences (1) between the chatbot personas within the no-choice group and (2) between the no-choice and the free-choice groups. Results While the main effect of the chatbot persona on affective bond and usage intentions was insignificant (P=.87), we found differences based on participants’ demographic profiles: main effects for gender (P=.04, ηp2=0.115) and age (P<.001, ηp2=0.192) and a significant interaction effect of persona and age (P=.01, ηp2=0.102). Participants younger than 40 years reported higher scores for affective bond and usage intentions for the interpersonally more distant expert and institution chatbots; participants 40 years or older reported higher outcomes for the closer peer and dialogical-self chatbots. The option to freely choose a persona significantly benefited perceptions of the peer chatbot further (eg, free-choice group affective bond: mean 5.28, SD 0.89; no-choice group affective bond: mean 4.54, SD 1.10; P=.003, ηp2=0.117). Conclusions Manipulating a chatbot’s social role is a possible avenue for health care chatbot designers to tailor clients’ chatbot experiences using user-specific demographic factors and to improve clients’ perceptions and behavioral intentions toward the chatbot. Our results also emphasize the benefits of letting clients freely choose between chatbots

    Spectators’ aesthetic experiences of sound and movement in dance performance

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    In this paper we present a study of spectators’ aesthetic experiences of sound and movement in live dance performance. A multidisciplinary team comprising a choreographer, neuroscientists and qualitative researchers investigated the effects of different sound scores on dance spectators. What would be the impact of auditory stimulation on kinesthetic experience and/or aesthetic appreciation of the dance? What would be the effect of removing music altogether, so that spectators watched dance while hearing only the performers’ breathing and footfalls? We investigated audience experience through qualitative research, using post-performance focus groups, while a separately conducted functional brain imaging (fMRI) study measured the synchrony in brain activity across spectators when they watched dance with sound or breathing only. When audiences watched dance accompanied by music the fMRI data revealed evidence of greater intersubject synchronisation in a brain region consistent with complex auditory processing. The audience research found that some spectators derived pleasure from finding convergences between two complex stimuli (dance and music). The removal of music and the resulting audibility of the performers’ breathing had a significant impact on spectators’ aesthetic experience. The fMRI analysis showed increased synchronisation among observers, suggesting greater influence of the body when interpreting the dance stimuli. The audience research found evidence of similar corporeally focused experience. The paper discusses possible connections between the findings of our different approaches, and considers the implications of this study for interdisciplinary research collaborations between arts and sciences

    An End-to-End Conversational Style Matching Agent

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    We present an end-to-end voice-based conversational agent that is able to engage in naturalistic multi-turn dialogue and align with the interlocutor's conversational style. The system uses a series of deep neural network components for speech recognition, dialogue generation, prosodic analysis and speech synthesis to generate language and prosodic expression with qualities that match those of the user. We conducted a user study (N=30) in which participants talked with the agent for 15 to 20 minutes, resulting in over 8 hours of natural interaction data. Users with high consideration conversational styles reported the agent to be more trustworthy when it matched their conversational style. Whereas, users with high involvement conversational styles were indifferent. Finally, we provide design guidelines for multi-turn dialogue interactions using conversational style adaptation

    Replika in the Metaverse: the moral problem with empathy in ‘It from Bit’

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    Enabling autistic sociality: unrealised potentials in two-sided social interaction

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    Research on autism, which is defined as a life-long developmental disability affecting social interaction, has focussed predominantly on how autistic individuals perceive and interact with others with less emphasis on the perspectives of their interactional partners. Yet autistic viewpoints have highlighted how other people are part of a two-way breakdown in interaction originating from differences between people rather than the deficit of any one individual, a phenomenon known as the double empathy problem. A gap therefore exists in the literature in terms of understanding how autistic sociality (i.e. the range of social opportunities possible for a given individual on the spectrum) is shaped by different interactional partners. This thesis examines the double empathy problem in three interactional contexts. Study 1 examines relationships between autistic people and their family members through focussing on perspective-taking, the ability to impute mental states to others. In light of prior research where autistic abilities have been assessed using abstract scenarios, Study 1 implements a two-way measure of perspective-taking which considers both sides of 22 real-life relationships (n=44) consisting of autistic adults and their family members, to understand how autistic people are seen by familiar others as well as vice versa. It uses a mixed-methods approach, where members of each dyad were individually asked about 12 topics, providing quantitative scores and qualitative explanation of their rating of Self, their rating of their partner, and their predicted rating by their partner. Comparison of perspectives provided a means for detecting misunderstandings and their underlying rationale. The contribution of Study 1 is that it shows perspective-taking is two-sided: family members can be biased in underestimating the perspective-taking of their autistic relatives, while autistic adults are aware of being negatively viewed despite disagreeing with such views. Study 2 examines interactions between autistic adults (n=30) partaking in a naturally occurring activity of video-gaming at a charity. It is a qualitative study using participant observation, with each conversational turn systematically rated in terms of coherence, affect and symmetry to identify the key features of neurodivergent intersubjectivity, the process through which autistic people build shared understanding in their own non-normative ways. The contribution of Study 2 is to identify two forms of neurodivergent intersubjectivity which enable shared understanding to be achieved, but which have traditionally been viewed as undesirable from a normative social viewpoint: a generous assumption of common ground that, when understood, lead to rapid rapport, and, when not understood, resulted in potentially disruptive utterances; and a low demand for coordination that ameliorated many challenges associated with disruptive turns. Study 3 examines interactions involving lay people (n=256) who believe they are interacting with an autistic partner through an online collaborative game, when in fact they are playing with an intelligent virtual agent (IVA) who behaves the same way for all participants. Its contribution is methodological as it develops a new application for simulating interactions in experimental research called Dyad3D. Study 3 uses Dyad3D to explore how disclosure of an autism diagnosis by the IVA affects social perception and social behaviour in comparison to a disclosure of dyslexia and a condition where there is no diagnostic disclosure. Combined with a post-game questionnaire, Study 3 triangulates self-reported (quantitative rating scales and qualitative explanation) and behavioural measures (quantitative scores of actions within the game) to understand the interplay of positive and negative discrimination elicited through using the label of autism. It highlights that diagnostic disclosure of autism leads to significant positive bias in social perception when compared to a disclosure of dyslexia or a no disclosure condition; yet participants are not as helpful towards the autistic IVA as they think they are, indicating a potential bias in helping behaviour. The thesis takes an abductive methodological approach which integrates with a wider call for a more participatory model of research in the study of autism. Abduction is a form of reasoning which involves the iterative development of a hypothesis that holds the best explanatory scope for the underlying phenomena observed. It is inherently aligned with a participatory model of research because abduction involves the ongoing exploration of ideas that may originate from multiple sources (i.e. interactions with autistic people as well as research outputs). Taking a more holistic approach to the development of knowledge with autistic people which recognises the legitimacy of different claims to knowledge is important, because prior research in the field has often failed to critically reflect on researcherparticipant positionality and the principals underlying the development of research agenda. For this reason, the thesis details the participatory activities which surround and interconnect with the development of the three empirical studies. Overall the thesis contributes to understanding autistic sociality as a dynamic, interactionally shaped process. It reasons that autistic people have unrealised social potential, both in terms of imagining other perspectives (Study 1) and coordinating with others (Study 2). However, such social potential may not be easily recognised by other non-autistic people who may be biased in their assumptions about autism (Study 1 and Study 3). Consequently, the evidence presented in this thesis helps to explain some of the processes that underscore the double empathy problems reported in literature, including poor mental health (because autistic people are aware that they are misunderstood by others, see Study 1), employment prospects (because autistic social potential is under-recognised by others, see Study 1 and 3), and quality of life (because neurotypical standards of communication are not compatible with neurodivergent forms of intersubjectivity, see Study 2). The thesis therefore makes suggestions for how we design enabling environments which are sensitive to the dynamic factors that can enable autistic sociality to flourish

    Artificial Empathy in Marketing Interactions: Bridging the Human-AI Gap in Affective and Social Customer Experience

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    Artificial intelligence (AI) continues to transform firm-customer interactions. However, current AI marketing agents are often perceived as cold and uncaring and can be poor substitutes for human-based interactions. Addressing this issue, this article argues that artificial empathy needs to become an important design consideration in the next generation of AI marketing applications. Drawing from research in diverse disciplines, we develop a systematic framework for integrating artificial empathy into AI-enabled marketing interactions. We elaborate on the key components of artificial empathy and how each component can be implemented in AI marketing agents. We further explicate and test how artificial empathy generates value for both customers and firms by bridging the AI-human gap in affective and social customer experience. Recognizing that artificial empathy may not always be desirable or relevant, we identify the requirements for artificial empathy to create value and deduce situations where it is unnecessary and, in some cases, harmful
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