Dark patterns are deceptive strategies that recent work in humancomputer interaction (HCI) has captured throughout digital domains, including social networking sites (SNSs).While research has identified difficulties among people to recognise dark patterns effectively, few studies consider vulnerable populations and their experience in this regard, including people with attention deficit hyperactivity disorder (ADHD), who may be especially susceptible to attention-grabbing tricks.Based on an interactive web study with 135 participants, we investigate SNS users' ability to recognise and avoid dark patterns by comparing results from participants with and without ADHD.In line with prior work, we noticed overall low recognition of dark patterns with no significant differences between the two groups.Yet, ADHD individuals were able to avoid specific dark patterns more often.Our results advance previous work by understanding dark patterns in a realistic environment and offer insights into their effect on vulnerable populations.

While Personal Informatics (PI) tools utilise data visualisations to communicate behaviour, users often struggle to make sense of their data and translate it to actionable insights. Conversational Agents (CAs) offer potential for improved access to PI data, yet their role in PI tools remains under-explored. We conducted a two-week user study with journals, interviews and logging with n = 36 participants using a novel commercial fitness tracker with an embedded CA. We identified the give and take principle as essential for meaningful sensemaking with a CA—a dynamic resulting in more effective interactions given users’ inputs ( give ) are met with prompts that are sufficiently specific and built upon prior data engagement ( take ). A critical point was how users perceived the CA during their initial interactions, with first impressions often determining further engagement. We contribute insights into how CAs can support or hinder the PI experience, offering implications for future PI system designs.

Mixed Reality (MR) integrates virtual objects with the real world, offering potential but raising concerns about misuse through dark patterns. This study explored the effects of four dark patterns, adapted from prior research, and applied to MR across three targets: places, products, and people. In a two-factorial within-subject study with 74 participants, we analyzed 13 videos simulating MR experiences during a city walk. Results show that all dark patterns significantly reduced user comfort, increased reactance, and decreased the intention to use MR glasses, with the most disruptive effects linked to personal or monetary manipulation. Additionally, the dark patterns of Emotional and Sensory Manipulation and Hiding Information produced similar impacts on the user in MR, suggesting a re-evaluation of current classifications to go beyond deceptive design techniques. Our findings highlight the importance of developing ethical design guidelines and tools to detect and prevent dark patterns as immersive technologies continue to evolve.

Deceptive and coercive design practices are increasingly used by companies to extract profit, harvest data, and limit consumer choice. Dark patterns represent the most common contemporary amalgamation of these problematic practices, connecting designers, technologists, scholars, regulators, and legal professionals in transdisciplinary dialogue. However, a lack of universally accepted definitions across the academic, legislative, practitioner, and regulatory space has likely limited the impact that scholarship on dark patterns might have in supporting sanctions and evolved design practices. In this paper, we seek to support the development of a shared language of dark patterns, harmonizing ten existing regulatory and academic taxonomies of dark patterns and proposing a three-level ontology with standardized definitions for 64 synthesized dark pattern types across low-, meso-, and high-level patterns. We illustrate how this ontology can support translational research and regulatory action, including transdisciplinary pathways to extend our initial types through new empirical work across application and technology domains.

Advances in natural language processing and understanding have led to a rapid growth in the popularity of conversational user interfaces (CUIs). While CUIs introduce novel benefits, they also yield risks that may exploit people’s trust. Although research looking at unethical design deployed through graphical user interfaces (GUIs) established a thorough understanding of so-called dark patterns, there is a need to continue this discourse within the CUI community to understand potentially problematic interactions. Addressing this gap, we interviewed 27 participants from three cohorts: researchers, practitioners, and frequent users of CUIs. Applying thematic analysis, we construct five themes reflecting each cohort’s insights about ethical design challenges and introduce the CUI Expectation Cycle, bridging system capabilities and user expectations while considering each theme’s ethical caveats. This research aims to inform future development of CUIs to consider ethical constraints while adopting a human-centred approach.

This study explores the prevalence of dark patterns in mobile games that exploit players through temporal, monetary, social, and psychological means. Recognizing the ethical concerns and potential harm surrounding these manipulative strategies, we analyze user-generated data of 1496 games to identify relationships between the deployment of dark patterns within "dark" and "healthy" games. Our findings reveal that dark patterns are not only widespread in games typically seen as problematic but are also present in games that may be perceived as benign. This research contributes needed quantitative support to the broader understanding of dark patterns in games. With an emphasis on ethical design, our study highlights current problems of revenue models that can be particularly harmful to vulnerable populations. To this end, we discuss the relevance of community-based approaches to surface harmful design and the necessity for collaboration among players/users and practitioners to promote healthier gaming experiences.

In the rapidly evolving field of artificial intelligence (AI) agents, designing the agent's characteristics is crucial for shaping user experience.This workshop aims to establish a research community focused on AI agent persona design for various contexts, such as in-car assistants, educational tools, and smart home environments.We will explore critical aspects of persona design, such as voice, embodiment, and demographics, and their impact on user satisfaction and engagement.Through discussions and hands-on activities, we aim to propose practices and standards that enhance the ecological validity of agent personas.Topics include the design of conversational interfaces, the influence of agent personas on user experience, and approaches for creating contextually appropriate AI agents.This workshop will provide a platform for building a community dedicated to developing AI agent personas that better fit diverse, everyday interactions.

Interest in unethical user interfaces has grown in HCI over recent years, with researchers identifying malicious design strategies referred to as “dark patterns”. While such strategies have been described in numerous domains, we lack a thorough understanding of how they operate in social networking services (SNSs). Pivoting towards regulations against such practices, we address this gap by offering novel insights into the types of dark patterns deployed in SNSs and people’s ability to recognise them across four widely used mobile SNS applications. Following a cognitive walkthrough, experts (N = 6) could identify instances of dark patterns in all four SNSs, including co-occurrences. Based on the results, we designed a novel rating procedure for evaluating the malice of interfaces. Our evaluation shows that regular users (N = 193) could differentiate between interfaces featuring dark patterns and those without. Such rating procedures could support policymakers’ current moves to regulate deceptive and manipulative designs in online interfaces.

Research in HCI has shown a growing interest in unethical design practices across numerous domains, often referred to as “dark patterns”. There is, however, a gap in related literature regarding social networking services (SNSs). In this context, studies emphasise a lack of users’ self-determination regarding control over personal data and time spent on SNSs. We collected over 16 hours of screen recordings from Facebook’s, Instagram’s, TikTok’s, and Twitter’s mobile applications to understand how dark patterns manifest in these SNSs. For this task, we turned towards HCI experts to mitigate possible difficulties of non-expert participants in recognising dark patterns, as prior studies have noticed. Supported by the recordings, two authors of this paper conducted a thematic analysis based on previously described taxonomies, manually classifying the recorded material while delivering two key findings: We observed which instances occur in SNSs and identified two strategies — engaging and governing — with five dark patterns undiscovered before.

This paper analyses Human-Computer Interaction (HCI) literature reviews to provide a clear conceptual basis for authors, reviewers, and readers. HCI is multidisciplinary and various types of literature reviews exist, from systematic to critical reviews in the style of essays. Yet, there is insufficient consensus of what to expect of literature reviews in HCI. Thus, a shared understanding of literature reviews and clear terminology is needed to plan, evaluate, and use literature reviews, and to further improve review methodology. We analysed 189 literature reviews published at all SIGCHI conferences and ACM Transactions on Computer-Human Interaction (TOCHI) up until August 2022. We report on the main dimensions of variation: (i) contribution types and topics; and (ii) structure and methodologies applied. We identify gaps and trends to inform future meta work in HCI and provide a starting point on how to move towards a more comprehensive terminology system of literature reviews in HCI.

Reflecting on personal challenges can be difficult. Without encouragement, the reflection process often remains superficial, thus inhibiting deeper understanding and learning from past experiences. To allow people to immerse themselves in and deeply reflect on past challenges, we developed SelVReflect, a VR experience which offers active voice-based guidance and a space to freely express oneself. SelVReflect was developed in an iterative design process (N=5) and evaluated in a user study with N=20 participants. We found that SelVReflect enabled participants to approach their challenge and its (emotional) components from different perspectives and to discover new relationships between these components. By making use of the spatial possibilities in VR, participants developed a better understanding of the situation and of themselves. We contribute empirical evidence of how a guided VR experience can support reflection. We discuss opportunities and design requirements for guided VR experiences that aim to foster deeper reflection.

While a growing number of households contain home assistants, they mainly remain voice-only devices where the virtual agent is not represented visually. The visual representation of the agent is limited to the device's housing and abstract light animations that signify the assistant's state to its users. However, the audio channel is limited in conveying information beyond semantic content. Embodied virtual assistants can enhance interaction with conversational interfaces by adding a visual layer to further convey personality and human characteristics. In this work, we conducted an online survey (N=78) to explore people's preferences for visualizing humanoid assistants. Our findings suggest that participants prefer an agent who appears mature, healthy, competent, and attractive. Furthermore, demographic similarities between the users and agents are wished for the agent to look more relatable. We discuss these findings and their implications for the design of virtual humanoid home assistants.