Effect of Explanation Conceptualisations on Trust in AI-assisted Credibility AssessmentAs misinformation increasingly proliferates on social media platforms, it has become crucial to explore how to best convey automated news credibility assessments to end-users, and foster trust in fact-checking AIs. In this paper, we investigate how model-agnostic, natural language explanations influence trust and reliance on a fact-checking AI. We construct explanations from four Conceptualisation Validations (CVs) – namely consensual, expert, internal (logical), and empirical – which are foundational units of evidence that humans utilise to validate and accept new information. Our results show that providing explanations significantly enhances trust in AI, even in a fact-checking context where influencing pre-existing beliefs is often challenging, with different CVs causing varying degrees of reliance. We find consensual explanations to be the least influential, with expert, internal, and empirical explanations exerting twice as much influence. However, we also find that users could not discern whether the AI directed them towards the truth, highlighting the dual nature of explanations to both guide and potentially mislead. Further, we uncover the presence of automation bias and aversion during collaborative fact-checking, indicating how users' previously established trust in AI can moderate their reliance on AI judgements. We also observe the manifestation of a 'boomerang'/backfire effect often seen in traditional corrections to misinformation, with individuals who perceive AI as biased or untrustworthy doubling down and reinforcing their existing (in)correct beliefs when challenged by the AI. We conclude by presenting nuanced insights into the dynamics of user behaviour during AI-based fact-checking, offering important lessons for social media platforms.2024SPSaumya Pareek et al.Session 3e: Trust and Understanding in Explainable AICSCW
Reflected Reality: Augmented Reality through the MirrorZhou等人提出了Reflected Reality系统，利用镜子作为AR交互界面，在反射环境中实现虚拟内容的手势交互与观察。2024QZQiushi Zhou et al.AR Navigation & Context AwarenessUbiComp
The Jamais Vu Effect: Understanding the Fragile Illusion of Co-presence in Mixed RealityCollaboration in distributed mixed reality (MR) creates co-presence illusions by bringing a digital representation of the remote collaborator into the local user's physical space. However, constraints in each physical environment, such as different spatial layouts and furniture, can hinder collaboration and momentarily breaks the illusion of co-location. Since the remote user's physical space is invisible to the local user, the source of the interaction problem is often imperceptible, leading to familiar real-world interactions suddenly feeling unfamiliar---a concept we term the Jamais Vu Effect. In this paper, we conceptualise and demonstrate this effect through a five-part user study that elicits the user experience of the Jamais Vu Effect in MR. We contribute a shared vocabulary and understanding of spatial interface design challenges to help designers and researchers discuss and improve distributed collaboration in MR.2024EWEmily Wong et al.Mixed Reality WorkspacesImmersion & Presence ResearchDIS
Augmented Reality at Zoo Exhibits: A Design Framework for Enhancing the Zoo ExperienceAugmented Reality (AR) offers unique opportunities for contributing to zoos' objectives of public engagement and education about animal and conservation issues. However, the diversity of animal exhibits pose challenges in designing AR applications that are not encountered in more controlled environments, such as museums. To support the design of AR applications that meaningfully engage the public with zoo objectives, we first conducted two scoping reviews to interrogate previous work on AR and broader technology use at zoos. We then conducted a workshop with zoo representatives to understand the challenges and opportunities in using AR to achieve zoo objectives. Additionally, we conducted a field trip to a public zoo to identify exhibit characteristics that impacts AR application design. We synthesise the findings from these studies into a framework that enables the design of diverse AR experiences. We illustrate the utility of the framework by presenting two concepts for feasible AR applications.2024BSBrandon Victor Syiem et al.Queensland University of TechnologyAR Navigation & Context AwarenessMuseum & Cultural Heritage DigitizationCHI
Practice-informed Patterns for Organising Large Groups in Distributed Mixed Reality CollaborationCollaborating across dissimilar, distributed spaces presents numerous challenges for computer-aided spatial communication. Mixed reality (MR) can blend selected surfaces, allowing collaborators to work in blended f-formations (facing formations), even when their workstations are physically misaligned. Since collaboration often involves more than just participant pairs, this research examines how we might scale MR experiences for large-group collaboration. To do so, this study recruited collaboration designers (CDs) to evaluate and reimagine MR for large-scale collaboration. These CDs were engaged in a four-part user study that involved a technology probe, a semi-structured interview, a speculative low-fidelity prototyping activity and a validation session. The outcomes of this paper contribute (1) a set of collaboration design principles to inspire future computer-supported collaborative work, (2) eight collaboration patterns for blended f-formations and collaboration at scale and (3) theoretical implications for f-formations and space-place relationships. As a result, this work creates a blueprint for scaling collaboration across distributed spaces.2024EWEmily Wong et al.The University of MelbourneMixed Reality WorkspacesDistributed Team CollaborationCHI
The Effects of Generative AI on Design Fixation and Divergent ThinkingGenerative AI systems have been heralded as tools for augmenting human creativity and inspiring divergent thinking, though with little empirical evidence for these claims. This paper explores the effects of exposure to AI-generated images on measures of design fixation and divergent thinking in a visual ideation task. Through a between-participants experiment (N=60), we found that support from an AI image generator during ideation leads to higher fixation on an initial example. Participants who used AI produced fewer ideas, with less variety and lower originality compared to a baseline. Our qualitative analysis suggests that the effectiveness of co-ideation with AI rests on participants' chosen approach to prompt creation and on the strategies used by participants to generate ideas in response to the AI's suggestions. We discuss opportunities for designing generative AI systems for ideation support and incorporating these AI tools into ideation workflows.2024SWSamangi Wadinambiarachchi et al.University of MelbourneGenerative AI (Text, Image, Music, Video)AI-Assisted Creative WritingCHI
Exploring the Association between Moral Foundations and Judgements of AI BehaviourHow do individual differences in personal morality affect perceptions and judgments of morally contentious behaviours from AI systems? By applying Moral Foundations Theory (MFT) to the context of AI, this study sought to develop a predictive Bayesian model for assessing moral judgements based on individual differences in moral constitution. Participants (N=240) were asked to assess six different scenarios, carefully designed to elicit reflection on the behaviour of AI systems. Together, with results from the Moral Foundations Questionnaire, we performed both Bayesian modelling and reflexive thematic analysis to investigate the associations between individual differences in moral foundations and judgements of the AI systems. Results revealed a mild association between individual MFT scores and judgments of AI behaviours. Qualitative responses suggested a participant’s technical understanding of AI systems, rather than intrinsic moral values, predominantly influenced their judgments, with those who judged the behaviour as wrong tending to anthropomorphise the AI systems behaviour.2024JBJoe Brailsford et al.The University of MelbourneAI Ethics, Fairness & AccountabilityAlgorithmic Transparency & AuditabilityAlgorithmic Fairness & BiasCHI
Blended Whiteboard: Physicality and Reconfigurability in Remote Mixed Reality CollaborationThe whiteboard is essential for collaborative work. To preserve its physicality in remote collaboration, Mixed Reality (MR) can blend real whiteboards across distributed spaces. Going beyond reality, MR can further enable interactions like panning and zooming in a virtually reconfigurable infinite whiteboard. However, this reconfigurability conflicts with the sense of physicality. To address this tension, we introduce Blended Whiteboard, a remote collaborative MR system enabling reconfigurable surface blending across distributed physical whiteboards. Blended Whiteboard supports a unique collaboration style, where users can sketch on their local whiteboards but also reconfigure the blended space to facilitate transitions between loosely and tightly coupled work. We describe design principles inspired by proxemics; supporting users in changing between facing each other and being side-by-side, and switching between navigating the whiteboard synchronously and independently. Our work shows exciting benefits and challenges of combining physicality and reconfigurability in the design of distributed MR whiteboards.2024JGJens Emil Grønbæk et al.Aarhus University, University of MelbourneMixed Reality WorkspacesDistributed Team CollaborationCHI
AI-Driven Mediation Strategies for Audience Depolarisation in Online DebatesOnline polarisation can tear the fabric of civility through reinforcing social media's perceptions of division and discord. Social media platforms often rely on content-moderation to combat polarisation, contingent on the reactive removal or flagging of content. However, this approach often remains agnostic of the underlying debate's ideas and stifles open discourse. In this study, we use prompt-tuned language models to mediate social media debates, applying the strategies of the Thomas-Kilmann Conflict Mode Instrument (TKI). We evaluate multiple mediation strategies in providing targeted responses to the debates, as shown to a debate audience. Our findings show that high-cooperativeness TKI strategies offered more persuasive arguments, while an accommodating argument strategy was the most successful at depolarising the audience's opinion. Furthermore, high-cooperativeness strategies also increased the perception that the debaters will reach a consensus. Our work paves the way for scalable and personalised tools that mediate social media debates to encourage depolarisation.2024JGJarod Govers et al.University of MelbourneHuman-LLM CollaborationAI Ethics, Fairness & AccountabilityAlgorithmic Fairness & BiasCHI
Here and Now: Creating Improvisational Dance Movements with a Mixed Reality MirrorThis paper explores using mixed reality (MR) mirrors for supporting improvisational dance making. Motivated by the prevalence of mirrors in dance studios and inspired by Forsythe’s Improvisation Technologies, we conducted workshops with 13 dancers and choreographers to inform the design of future MR visualisation and annotation tools for dance. The workshops involved using a prototype MR mirror as a technology probe that reveals the spatial and temporal relationships between the reflected dancing body and its surroundings during improvisation; speed dating group interviews around future design ideas; follow-up surveys and extended interviews with a digital media dance artist and a dance educator. Our findings highlight how the MR mirror enriches dancers' temporal and spatial perception, creates multi-layered presence, and affords appropriation by dancers. We also discuss the unique place of MR mirrors in the theoretical context of dance and in the history of movement visualisation, and distil lessons for broader HCI research.2023QZQiushi Zhou et al.University of MelbourneMixed Reality WorkspacesDigital Art Installations & Interactive PerformanceDance & Body Movement ComputingCHI
Modeling Temporal Target Selection: A Perspective from Its Spatial CorrespondenceTemporal target selection requires users to wait and trigger the selection input within a bounded time window, with a selection cursor that is expected to be delayed. This task conceptualizes, for example, a variety of game scenarios such as determining the timing of shooting a projectile towards a moving object. In this work, we explore models that predict "when'' users typically perform a selection (i.e., user selection distribution) and their selection error rates in such tasks. We hypothesize that users react to temporal factors including "distance'', "width'', and "delay'' as how they treat the corresponding variables in spatial target selection. The derived models are evaluated in a controlled experiment and an MTurk-based online study. Our research contributes new knowledge on user behavior in temporal target selection tasks and its potential connection with its spatial correspondence. Our models and conclusions can benefit both users and designers of relevant interactive applications.2023DYDifeng Yu et al.University of MelbourneHuman Pose & Activity RecognitionVisualization Perception & CognitionGamification DesignCHI
Partially Blended Realities: Aligning Dissimilar Spaces for Distributed Mixed Reality MeetingsMixed Reality allows for distributed meetings where people's local physical spaces are virtually aligned into blended interaction spaces. In many cases, people's physical rooms are dissimilar, making it challenging to design a coherent blended space. We introduce the concept of Partially Blended Realities (PBR) --- using Mixed Reality to support remote collaborators in partially aligning their physical spaces. As physical surfaces are central in collaborative work, PBR supports users in transitioning between different configurations of tables and whiteboard surfaces. In this paper, we 1) describe the design space of PBR, 2) present RealityBlender to explore interaction techniques for how users may configure and transition between blended spaces, and 3) provide insights from a study on how users experience transitions in a remote collaboration task. With this work, we demonstrate new potential for using partial solutions to tackle the alignment problem of dissimilar spaces in distributed Mixed Reality meetings.2023JGJens Emil Sloth Grønbæk et al.Aarhus UniversityMixed Reality WorkspacesDistributed Team CollaborationCHI
Volumetric Mixed Reality Telepresence for Real-time Cross Modality CollaborationMixed-reality telepresence allows local and remote users feel as if they are present together in the same space. In this paper we report on a mixed-reality volumetric telepresence system that is adaptable, multi-user and cross-modal, i.e. combining augmented and virtual reality technologies with face-to-face interactions. The system extends state-of-art by creating full-body and environmental volumetric renderings in real-time over local enterprise networks. We report findings of an evaluation in a training scenario which was adapted for remote delivery and led by an industry professional. Analysis of interviews and observed behaviours identify varying attitudes towards virtually mediated full-body experiences and highlight the impact of volumetric mixed-reality telepresence to facilitate personal experiences of co-presence and to ground communication with interlocutors.2023AIAndrew Irlitti et al.University of MelbourneMixed Reality WorkspacesImmersion & Presence ResearchTeleoperation & TelepresenceCHI
What's the Appeal? Perceptions of Review Processes for Algorithmic DecisionsIf you were significantly impacted by an algorithmic decision, how would you want the decision to be reviewed? In this study, we explore perceptions of review processes for algorithmic decisions that differ across three dimensions: the reviewer, how the review is conducted, and how long the review takes. Using a choice-based conjoint analysis we find that people prefer review processes that provide for human review, the ability to participate in the review process, and a timely outcome. Using a survey, we find that people also see human review that provides for participation to be the fairest review process. Our qualitative analysis indicates that the fairest review process provides the greatest likelihood of a favourable outcome, an opportunity for the decision subject and their situation to be fully and accurately understood, human involvement, and dignity. These findings have implications for the design of contestation procedures and also the design of algorithmic decision-making processes.2022HLHenrietta Lyons et al.University of MelbourneExplainable AI (XAI)Algorithmic Transparency & AuditabilityCHI
Integrating Gaze and Speech for Enabling Implicit InteractionsGaze and speech are rich contextual sources of information that, when combined, can result in effective and rich multimodal interactions. This paper proposes a machine learning-based pipeline that leverages and combines users’ natural gaze activity, the semantic knowledge from their vocal utterances and the synchronicity between gaze and speech data to facilitate users’ interaction. We evaluated our proposed approach on an existing dataset, which involved 32 participants recording voice notes while reading an academic paper. Using a Logistic Regression classifier, we demonstrate that our proposed multimodal approach maps voice notes with accurate text passages with an average 𝐹1-Score of 0.90. Our proposed pipeline motivates the design of multimodal interfaces that combines natural gaze and speech patterns to enable robust interactions2022AKAnam Ahmad Khan et al.The University of MelbourneEye Tracking & Gaze InteractionVoice User Interface (VUI) DesignCHI
To Type or To Speak? The Effect of Input Modality on Text Understanding During Note-takingThough recent technological advances have enabled note-taking through different modalities (e.g., keyboard, digital ink, voice), there is still a lack of understanding of the effect of the modality choice on learning. In this paper, we compared two note-taking input modalities—keyboard and voice—to study their effects on participants’ understanding of learning content. We conducted a study with 60 participants in which they were asked to take notes using voice or keyboard on two independent digital text passages while also making a judgment about their performance on an upcoming test. We built mixed-effects models to examine the effect of the note-taking modality on learners’ text comprehension, the content of notes and their meta-comprehension judgement. Our findings suggest that taking notes using voice leads to a higher conceptual understanding of the text when compared to typing the notes. We also found that using voice triggers generative processes that result in learners taking more elaborate and comprehensive notes. The findings of the study imply that note-taking tools designed for digital learning environments could incorporate voice as an input modality to promote effective note-taking and a higher conceptual understanding of the text.2022AKAnam Ahmad Khan et al.The University of MelbourneVoice User Interface (VUI) DesignIntelligent Tutoring Systems & Learning AnalyticsCHI
Conceptualising Contestability: Perspectives on Contesting Algorithmic DecisionsAs the use of algorithmic systems in high-stakes decision-making increases, the ability to contest algorithmic decisions is being recognised as an important safeguard for individuals. Yet, there is little guidance on what `contestability'--the ability to contest decisions--in relation to algorithmic decision-making requires. Recent research presents different conceptualisations of contestability in algorithmic decision-making. We contribute to this growing body of work by describing and analysing the perspectives of people and organisations who made submissions in response to Australia's proposed `AI Ethics Framework', the first framework of its kind to include `contestability' as a core ethical principle. Our findings reveal that while the nature of contestability is disputed, it is seen as a way to protect individuals, and it resembles contestability in relation to human decision-making. We analyse and consider the implications of these findings.2021HLHenrietta Lyons et al.Algorithms and Decision MakingCSCW
A Probabilistic Interpretation of Motion Correlation Selection TechniquesMotion correlation interfaces are those that present targets moving in different patterns, which the user can select by matching their motion. In this paper, we re-formulate the task of target selection as a probabilistic inference problem. We demonstrate that previous interaction techniques can be modelled using a Bayesian approach and that how modelling the selection task as transmission of information can help us make explicit the assumptions behind similarity measures. We propose ways of incorporating uncertainty into the decision-making process and demonstrate how the concept of entropy can illuminate the measurement of the quality of a design. We apply these techniques in a case study and suggest guidelines for future work.2021EVEduardo Velloso et al.University of MelbourneHuman Pose & Activity RecognitionComputational Methods in HCICHI
A Critique of Electrodermal Activity Practices at CHIElectrodermal activity data is widely used in HCI to capture rich and unbiased signals. Results from related fields, however, have suggested several methodological issues that can arise when practices do not follow established standards. In this paper, we present a systematic methodological review of CHI papers involving the use of EDA data according to best practices from the field of psychophysiology, where standards are well-established and mature. We found severe issues in our sample at all stages of the research process. To ensure the validity of future research, we highlight pitfalls and offer directions for how to improve community standards.2021EBEbrahim Babaei et al.University of MelbourneBiosensors & Physiological MonitoringResearch Ethics & Open ScienceCHI
Impact of Task on Attentional Tunneling in Handheld Augmented RealityAttentional tunneling describes a phenomenon in Augmented Reality (AR) where users excessively focus on virtual content while neglecting their physical surroundings. This leads to the concern that users could neglect hazardous situations when using AR applications. However, studies have often confounded the role of the virtual content with the role of the associated task in inducing attentional tunneling. In this paper, we disentangle the impact of the associated task and of the virtual content on the attentional tunneling effect by measuring reaction times to events in two user studies. We found that presenting virtual content did not significantly increase user reaction times to events, but adding a task to the content did. This work contributes towards our understanding of the attentional tunneling effect on handheld AR devices, and highlights the need to consider both task and context when evaluating AR application usage.2021BSBrandon Victor Syiem et al.The University of MelbourneAR Navigation & Context AwarenessImmersion & Presence ResearchCHI