Dominik Pins

Emotion Artificial Intelligence (AI) is transforming the capabilities of personal assistant by enabling real-time adaptation to user emotions, behaviours, and contextual needs. This paper explores the potential of emotion-adaptive eco-feedback in personal assistant, particularly within home environments, to foster well-being, energy efficiency, and personalised user experiences. Currently, there is limited research on how users perceive emotion-adaptive eco-feedback and how emotion AI can be adopted in the eco-feedback within personal assistant in real-world settings. To address this, we employed a co-design method — Matchmaking for AI — to facilitate collaboration between real users and researchers. We built a living lab with 11 participants in Germany for half a year and conducted two experimental sessions: a pre-interview to understand user behaviours, requirements, and expectations on eco-feedback, and a co-design session using matchmaking for AI after half a year based on their appliance energy consumption data collected by our smart plugs using our open. DASH platform. The co-design sessions collaboratively brainstorm ideas for potential emotion AI adoptions and identify what their needs should be addressed by emotion AI technology. Through a co-design session, we generated eight design ideas that integrate emotion AI into eco-feedback. These concepts include emotion-adaptive eco-feedback framing, emotion-timed interaction and delivery and emotion-aware environment and social adaption. Our work explores the potential of using Emotion AI in eco-feedback within personal assistant and also provides new insights into AI co-design methodologies.

Artificial Intelligence (AI) is increasingly promoted as a solution for household energy management, yet adoption remains uneven due to infrastructural constraints, AI skepticism and privacy concerns. This paper explored the factors that shape AI adoption in Greek homes, situated within the Plegma Living Lab. The study engaged nine residents and six smart home experts in a two-phase participatory study to examine how lived practices and expert perspectives shape adoption. Our findings show that AI adoption is shaped by three key factors: awareness (driven by visibility and control over energy use), knowledge (enabled through personalised and explainable AI), and engagement (sustained by socially meaningful and motivating feedback). Experts highlighted tensions around predictability, affordability, ecosystem integration, and privacy. By bridging residents’ lived experiences with expert insights, we propose several design implications for supporting AI adoption in the home energy context. CCS Concepts: Human-centred computing → User models .

AI-powered systems pose unknown challenges for designers, policymakers, and users, making it more difficult to assess potential harms and outcomes. Although understanding risks is a requirement for building trust in technology, users are often excluded from risk assessments and explanations in policy and design. To address this issue, we conducted three workshops with 18 participants and discussed the EU AI Act, which is the European proposal for a legal framework for AI regulation. Based on results of these workshops, we propose a user-centered conceptual model with five risk dimensions (Design and Development, Operational, Distributive, Individual, and Societal) that includes 17 key risks. We further identify six criteria for categorizing use cases. Our conceptual model (1) contributes to responsible design discourses by connecting the risk assessment theories with user-centered approaches, and (2) supports designers and policymakers in more strongly considering a user perspective that complements their own expert views.

As voice assistants (VAs) become more advanced leveraging Large Language Models (LLMs) and natural language processing, their potential for accountable behavior expands. Yet, the long-term situational effectiveness of VAs’ accounts when errors occur remains unclear. In our 19-month exploratory study with 19 households, we investigated the impact of an Alexa feature that allows users to inquire about the reasons behind its actions. Our findings indicate that Alexa's accounts are often single, decontextualized responses that led to users’ alternative repair strategies over the long term, such as turning off the device, rather than initiating a dialogue about what went wrong. Through role-playing workshops, we demonstrate that VA interactions should facilitate explanatory dialogues as dynamic exchanges that consider a range of speech acts, recognizing users’ emotional states and the context of interaction. We conclude by discussing the implications of our findings for the design of accountable VAs.

In both data protection law and research of usable privacy, awareness and control over the collection and use of personal data are understood to be cornerstones of digital sovereignty. For example, the European General Data Protection Regulation (GDPR) provides data subjects with the right to access data collected by organisations but remains unclear on the concrete process design. However, the design of data subject rights is crucial when it comes to the ability of customers to exercise their right and fulfil regulatory aims such as transparency. To learn more about user needs in implementing the right to access as per GDPR, we conducted a two-step study. First, we defined a five-phase user experience journey regarding the right to access: finding, authentication, request, access and data use. Second, and based on this model, 59 participants exercised their right to access and evaluated the usability of each phase. Drawing on 422 datasets spanning 139 organisations, our results show several interdependencies of process design and user satisfaction. Thereby, our insights inform the community of usable privacy and especially the design of the right to access with a first, yet robust, empirical body.