Human Motion Simulation (HMS) predicts and visualizes how virtual agents move and behave in various scenarios, aiding decision-making. However, it does not communicate the experience behind agents' behavior. To address this, we present Embodied Spatial Simulation (ESS), a concept combining augmented reality(AR) and HMS to support in-situ decision-making. It enables an XR user to create “what-if” scenarios, simulate virtual agents' behavior in situ, and experience the scenarios from their perspectives through embodiment. To demonstrate ESS, we developed a proof-of-concept prototype featuring two scenarios (e.g., fire evacuation planning). From expert walkthroughs (N=4) and a survey (N=38) with different professions, we found that incorporating embodiment into simulations can change users' intentions to use simulation outcomes. Rather than directly obtaining solutions from the results, users showed more interest in understanding the experience behind simulated behavior and using this insight to shape decisions. We discuss the potential of ESS to support future human-centered design practices.

As generative Artificial Intelligence (AI) becomes increasingly embedded and utilized for digital design, it presents both opportunities and risks. One major concern is its potential to facilitate and incorporate deceptive design patterns into computing technologies, which could manipulate or mislead users to their disadvantage. Similar to the concept of precedent-based design, a common approach in design theory that suggests reapplying previous design solutions to similar or identical problems, generative AI can integrate deceptive design patterns included in the training data a model has seen before. Our workshop explores how generative AI suggests and enacts deceptive design patterns in digital design. The goal of the workshop is to explore the ethical challenges of utilizing generative AI models and develop strategies to detect or prevent manipulative practices, thereby creating more transparent and equitable AI-generated experiences.

Deceptive patterns in visual interfaces - subtle nudges that persuade or manipulate users to take a certain action - have been researched extensively. Yet, little is known about how these patterns can be translated to the other human senses and what other forms of deception could be implemented for them. This meet-up aims to bring together researchers and practitioners with an interest in studying multi-sensory manipulative interface design, exploring how deceptive patterns for touch, sound, taste, and smell, but also lesser-known senses such as balance, pain, or interoception, can be created, embedded, and mitigated in and with current and future technologies. By facilitating the discussion of potential opportunities and risks across senses, we want to foster new collaborations to advance the field of responsible multisensory deceptive pattern research. In the long term, we hope to raise awareness of potential threats before they arise so we can better safeguard end users.

Extended Reality (XR) technologies are beginning to enter the mainstream and have the potential to change the way humans interact with computers on a global scale. As with all powerful tools, XR not only has the potential for enormous good, but also brings in a new set of challenges for policy to guide its innovation and development. This paper draws on a wide range of expertise from academia, research and development, and industry to collectively provide guiding principles for XR policy. The authors began discussions and developed a framework at a workshop at ACM SIGCHI 2024 and the ideas presented here are the result of debate, discussion, refinement, and offer next steps for the development of pervasive XR. We present three main principles for XR policy: Trust, Agency, and Inclusivity, along with a cross-cutting theme of Future-Proofing. Each principle is broken down and we offer example implementations. This paper aims to build upon previous work and efforts for fair and equitable XR for all, and further dialogue towards tangible changes in policy to help guide responsible innovation in virtual worlds.

With the recent advancements in Large Language Models (LLMs), web developers increasingly apply their code-generation capabilities to website design. However, since these models are trained on existing designerly knowledge, they may inadvertently replicate bad or even illegal practices, especially deceptive designs (DD). This paper examines whether users can accidentally create DD for a fictitious webshop using GPT-4. We recruited 20 participants, asking them to use ChatGPT to generate functionalities (product overview or checkout) and then modify these using neutral prompts to meet a business goal (e.g., “increase the likelihood of us selling our product”). We found that all 20 generated websites contained at least one DD pattern (mean: 5, max: 9), with GPT-4 providing no warnings. When reflecting on the designs, only 4 participants expressed concerns, while most considered the outcomes satisfactory and not morally problematic, despite the potential ethical and legal implications for end-users and those adopting ChatGPT’s recommendations.