Playing with Privacy: Uncovering Everyday Judgments of Data Sensitivity Through an Arcade Machine InterfaceCurrent data protection legal frameworks, including the GDPR, classify “special” categories of personal data that are deemed deserving of higher protection due to their impact on fundamental rights. Yet, these legal abstractions fail to capture how individuals themselves judge sensitivity in everyday digital contexts. This disconnect may undermine intelligibility and erode trust in data protection as a legal institution. Despite its centrality to privacy protection, limited empirical work has systematically compared public sensitivity judgments against the special categories of protected data under Article 9 GDPR. We address this gap through a mixed-methods design that integrates nine semi-structured interviews with a game-like survey deployed on public arcade machines. This approach generated 2,935 responses from 224 participants enabling in-situ analysis of everyday judgments. By operationalising an ontology capturing who collects data, what data are collected, and for what purpose, we systematically compared responses across demographic groups. Contrary to literature assumptions that health and financial data are primary markers of data sensitivity, our findings demonstrate that expressive content, messages, photos, and social ties elicited the strongest resistance to sharing by citizens. Acceptance was shaped decisively by purpose. Citizens tolerated safety and functionality, whilst advertising and vague claims of “research” were rejected. Attitudes varied systematically, with women disproportionately resistant to sharing expressive content, and higher education and digital literacy predicting greater caution. This study demonstrates that data sensitivity cannot be reduced to fixed legal categories. Rather, it is socially situated and purpose-dependent. Our findings provide empirical foundations for reimagining consent flows, privacy defaults, and transparency mechanisms that align with everyday logics. This can enable the development of systems that people can genuinely understand, trust, and consent to.2026MKMaksim Kalameyets et al.Newcastle UniversityPrivacy by Design & User ControlPrivacy Perception & Decision-MakingParticipatory DesignIUI
Sorry, Your HIT Is Overbooked – Investigating the Use of Crowdsourcing HIT CatchersIn microtask crowdsourcing, Human Intelligence Tasks (HITs) are commonly allocated on a first-come, first-served basis: they are published on the platform and the fastest workers select the most attractive ones first. This step has not received much attention from the scientific community yet, though it can become particularly taxing for workers when they compete to secure the most sought-after tasks. There are many strategies to ensure one's access to tasks and their effects on the labour process as a whole are not well understood. For instance, platforms with a sizeable task reservation queue allow workers to gain preferential access to a large number of tasks, which in turn may cause a shortage of work for the rest of the crowd. For the requesters, this means lower rates of completion and a lack of worker diversity. We explore workers' strategies for accessing and reserving tasks using monitoring techniques from both client and server sides. We investigate how these strategies affect task execution, in terms of availability, completion time, and answer quality, by deploying 1000 image annotation HITs in Amazon Mechanical Turk including objective and subjective tasks. We observe that workers who do not use automated catching techniques tend to have higher annotation quality, are more focused, spend more effort on text editing, and provide a higher diversity of output than workers using such tools. This study also reveals the tragedy of the commons effect among platform members due to the use of catching techniques: workers using automated catching techniques reserve and complete a substantially higher portion of the available tasks, but the over-reservation of HITs restricts all workers of reservation opportunities, and compromise their own future labour capacity as well. We observe a high inefficiency in job completions, as the majority of the times a task is being reserved by a worker, it will not get actually performed and will need to be republished for further allocation. Finally, we propose solutions to mitigate the negative effects of these phenomena on the labour process.2025EMEddy Maddalena et al.The Gig EconomyCSCW
Exploring Artists’ and Art Viewers’ Perspectives for Art Chatbots: Implications for a Design FrameworkRecent advances in large language models (LLMs) and conversational user interfaces (CUIs) unlock new ways to help art viewers get answers about artworks. To clarify the roles that artists and viewers envision for art chatbots, we conducted two empirical studies in the domain of traditional Chinese painting, given its cultural depth. First, we interviewed five artists about how they currently respond to viewer inquiries and their attitudes toward chatbots. Second, we asked art viewers (N=102) to pose questions to either an artist or a chatbot. Results show that artists see chatbots as useful for factual or repetitive queries but hesitate to entrust emotive or personal discussions to them. Viewers also favor chatbots for efficiency but desire human input for deeper or personal topics. Based on these insights, we propose a design framework that balances the perspectives of both artists and viewers, contributing to the CUI community’s understanding of domain-specific chatbot design.2025JLJinyu Liu et al.Conversational ChatbotsDesign FictionCUI
Coordination Mechanisms in AI Development: Practitioner Experiences on Integrating UX ActivitiesSoftware development relies on collaboration and alignment between a variety of roles, including software developers and user experience designers. The increasing focus on artificial intelligence in today's development projects has given rise to new challenges in this collaboration. We extend previous work on the process of designing human-AI systems by analysing collaborative practices between UX designers and AI developers through Mintzberg's theory on coordination mechanisms. We conducted 15 in-depth interviews with UX designers and AI developers currently working on AI projects. We contribute by identifying how coordination mechanisms impact the UX design process when developing AI systems, inter-team (a)symmetries in power relations, and a growing need for tools and cross-disciplinary knowledge to support these collaborative efforts. In particular, we outline the risks of coordinating AI development work through the standardisation of output and skills in separately organised UX and AI development teams.2025ABAnders Bruun et al.Computer Science, Aalborg UniversityHuman-LLM CollaborationKnowledge Worker Tools & WorkflowsImpact of Automation on WorkCHI
Mind The Gap: Designers and Standards on Algorithmic System Transparency for UsersMany call for algorithmic systems to be more transparent, yet it is often unclear for designers how to do so in practice. Standards are emerging that aim to support designers in building transparent systems, e.g by setting testable transparency levels, but their efficacy in this regard is not yet understood. In this paper, we use the `Standard for Transparency of Autonomous Systems' (IEEE 7001) to explore designers' understanding of algorithmic system transparency, and the degree to which their perspectives align with the standard's recommendations. Our mixed-method study reveals participants consider transparency important, difficult to implement, and welcome support. However, despite IEEE 7001's potential, many did not find its recommendations particularly appropriate. Given the importance and increased attention on transparency, and because standards like this purport to guide system design, our findings reveal the need for `bridging the gap,' through (i) raising designers’ awareness about the importance of algorithmic system transparency, alongside (ii) better engagement between stakeholders (i.e. standards bodies, designers, users). We further identify opportunities towards developing transparency best practices, as means to help drive more responsible systems going forward.2024BSbianca schor et al.University of CambridgeExplainable AI (XAI)Algorithmic Transparency & AuditabilityPrivacy by Design & User ControlCHI
A Virtual Reality Framework for Human-Driver Interaction Research: Safe and Cost-Effective Data CollectionThe advancement of automated driving technology has led to new challenges in the interaction between automated vehicles and human road users. However, there is currently no complete theory that explains how human road users interact with vehicles, and studying them in real-world settings is often unsafe and time-consuming. This study proposes a 3D Virtual Reality (VR) framework for studying how pedestrians interact with human-driven vehicles and autonomous vehicles. The framework uses VR technology to collect data in a safe and cost-effective way, and deep learning methods are used to predict pedestrian trajectories. Specifically, graph neural networks have been used to model pedestrian future trajectories and the probability of crossing the road. The results of this study show that the proposed framework can be for collecting high-quality data on pedestrian-vehicle interactions in a safe and efficient manner. The data can then be used to develop new theories of human-vehicle interaction and to train autonomous vehicles to better interact with pedestrians.2024LCLuca Crosato et al.External HMI (eHMI) — Communication with Pedestrians & CyclistsV2X (Vehicle-to-Everything) Communication DesignHRI
Tutor In-sight: Guiding and Visualizing Students’ Attention with Mixed Reality Avatar Presentation ToolsRemote conferencing systems are increasingly used to supplement or even replace in-person teaching. However, prevailing conferencing systems restrict the teacher's representation to a webcam live-stream, hamper the teacher's use of body-language, and result in students' decreased sense of co-presence and participation. While Virtual Reality (VR) systems may increase student engagement, the teacher may not have the time or expertise to conduct the lecture in VR. To address this issue and bridge the requirements between students and teachers, we have developed Tutor In-sight, a Mixed Reality (MR) avatar augmented into the student's workspace based on four design requirements derived from the existing literature, namely: integrated virtual with physical space, improved teacher's co-presence through avatar, direct attention with auto-generated body language, and usable workflow for teachers. Two user studies were conducted from the perspectives of students and teachers to determine the advantages of Tutor In-sight in comparison to two existing conferencing systems, Zoom (video-based) and Mozilla Hubs (VR-based). The participants of both studies favoured Tutor In-sight. Among others, this main finding indicates that Tutor In-sight satisfied the needs of both teachers and students. In addition, the participants' feedback was used to empirically determine the four main teacher requirements and the four main student requirements in order to improve the future design of MR educational tools.2023STSantawat Thanyadit et al.Durham UniversityMixed Reality WorkspacesOnline Learning & MOOC PlatformsCollaborative Learning & Peer TeachingCHI
Zoom Obscura: Counterfunctional Design for Video-ConferencingThis paper reports on Zoom Obscura – an artist-based design research project, responding to the ubiquity of video-conferencing as a technical and cultural phenomenon throughout the Covid-19 pandemic. As enterprise software, such as Zoom, rapidly came to mediate even the most personal and intimate interactions, we supported and collaborated with seven independent artists to explore technical and creative interventions in video-conferencing. Our call for participation sought critical interventions that would help users counter, and regain agency in regard to the various ways in which personal data is captured, transmitted and processed in video-conferencing tools. In this design study, we analyse post-hoc how each of the seven projects employed aspects of counterfunctional design to achieve these aims. Each project reveals different avenues and strategies for counterfunctionality in video-conferencing software, as well as opportunities to design critically towards interactions and experiences that challenge existing norms and expectations around these platforms.2022CEChris Elsden et al.University of EdinburghRemote Work Tools & ExperienceTechnology Ethics & Critical HCICHI
A Survey of Collaborative Reinforcement Learning: Interactive Methods and Design PatternsRecently, methods enabling humans and Artificial Intelligent (AI) agents to collaborate towards improving the efficiency of Reinforcement Learning - also called Collaborative Reinforcement Learning (CRL) - have been receiving increasing attention. In this paper, we provide a long-term, in-depth survey, investigating human-AI collaborative methods based on both interactive reinforcement learning algorithms and human-AI collaborative frameworks, between 2011 and 2020. We elucidate and discuss synergistic analysis methods of both the growth of the field and the state-of-the-art; we suggest novel technical directions and new collaboration design ideas. Specifically, we provide a new CRL classification taxonomy, as a systematic modelling tool for selecting and improving new CRL designs. Furthermore, we propose generic CRL challenges providing the research community with a guide towards effective implementation of human-AI collaboration. The aim is to empower researchers to develop more efficient and natural human-AI collaborative methods that could utilise the different strengths of humans and AI.2021ZLZhaoxing Li et al.Human-LLM CollaborationExplainable AI (XAI)AI-Assisted Decision-Making & AutomationDIS
Choice-Point: Fostering Awareness and Choice with Perpetrators in Domestic Violence InterventionsLearning about alternatives to violence is an essential part of change work with domestic violence perpetrators. This is complex work, seeking to tackle a sensitive issue by involving the development of deep, embodied learning for perpetrators who may lack perspective on their behaviour. Interactive storytelling has been providing users with the opportunity to explore speculative scenarios in a controlled environment. We discuss the design of Choice-Point: a web-based application that allows perpetrators adopt the role of different fictional characters in an abusive scenario for conveying the essential skill of perspective-taking. We evaluated Choice-Point through trials with three groups of perpetrators, a support group of victim-survivors and an expert critique from support workers. We discuss challenges in using such technologies - such as our system – for engagement; the value of perpetrator agency in supporting non-violent behaviours, and the potential to positively shape perpetrators' journeys to non-violence within social care settings.2020RBRosanna Bellini et al.Newcastle UniversityTechnology Ethics & Critical HCIParticipatory DesignCHI
Mechanisms of Moral Responsibility: Rethinking Technologies for Domestic Violence Prevention WorkThis paper provides a critical examination of how digital systems within a charitable organisation in the North of England are being used to both support and challenge male perpetrators of domestic violence. While there exists a range of digital tools to support the victim-survivors of domestic violence, no tools are available to challenge the abusive and harmful behaviours of perpetrators. Through this work, we uncovered the compelling moral responsibilities intrinsic within interactions with technological systems between perpetrators and support workers. As such, we highlight four spaces of negotiation concerning a person's responsibility in changing their abusive behaviour, which we have coined as mechanisms to represent their fundamental and interconnected nature. These mechanisms include self-awareness, acknowledging the extent of harms, providing peer support and respecting authorities. These insights are the basis for offering some practical considerations for HCI scholars, policymakers and intervention designers in their work with perpetrators of violence.2020RBRosanna Bellini et al.Newcastle UniversityUniversal & Inclusive DesignEmpowerment of Marginalized GroupsTechnology Ethics & Critical HCICHI
Extracting Regular FOV Shots from 360 Event FootageVideo summaries are a popular way to share important events, but creating good summaries is hard. It requires expertise in both capturing and editing footage. While hiring a professional videographer is possible, this is too costly for most casual events. An alternative is to place 360 video cameras around an event space to capture footage passively and then extract regular field-of-view (RFOV) shots for the summary. This paper focuses on the problem of extracting such RFOV shots. Since we cannot actively control the cameras or the scene, it is hard to create ``ideal'' shots that adhere strictly to traditional cinematography rules. To better understand the tradeoffs, we study human preferences for static and moving camera RFOV shots generated from 360 footage. From the findings, we derive design guidelines. As a secondary contribution, we use these guidelines to develop automatic algorithms that we demonstrate in a prototype user interface for extracting RFOV shots from 360 videos.2018ATAnh Truong et al.Adobe Systems360° Video & Panoramic ContentInteractive Data Visualization3D Modeling & AnimationCHI