Stevens, Gunnar
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Stevens, Gunnar
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Publication Access status: Metadata only , “Foggy sounds like nothing” — enriching the experience of voice assistants with sonic overlays(2023); ; Although Voice Assistants are ubiquitously available for some years now, the interaction is still monotonous and utilitarian. Sound design offers conceptual and methodological research to design auditive interfaces. Our work aims to complement and supplement voice interaction with sonic overlays to enrich the user experience. Therefore, we followed a user-centered design process to develop a sound library for weather forecasts based on empirical results from a user survey of associative mapping. After analyzing the data, we created audio clips for seven weather conditions and evaluated the perceived combination of sound and speech with 15 participants in an interview study. Our findings show that supplementing speech with soundscapes is a promising concept that communicates information and induces emotions with a positive affect for the user experience of Voice Assistants. Besides a novel design approach and a collection of sound overlays, we provide four design implications to support voice interaction designers.Source Type:Article1 - Some of the metrics are blocked by yourconsent settings
Publication Access status: Metadata only , An Overview of the Empirical Evaluation of Explainable AI (XAI): A Comprehensive Guideline for User-Centered Evaluation in XAI(2024); ; Recent advances in technology have propelled Artificial Intelligence (AI) into a crucial role in everyday life, enhancing human performance through sophisticated models and algorithms. However, the focus on predictive accuracy has often resulted in opaque black-box models that lack transparency in decision-making. To address this issue, significant efforts have been made to develop explainable AI (XAI) systems that make outcomes comprehensible to users. Various approaches, including new concepts, models, and user interfaces, aim to improve explainability, build user trust, enhance satisfaction, and increase task performance. Evaluation research has emerged to define and measure the quality of these explanations, differentiating between formal evaluation methods and empirical approaches that utilize techniques from psychology and human–computer interaction. Despite the importance of empirical studies, evaluations remain underutilized, with literature reviews indicating a lack of rigorous evaluations from the user perspective. This review aims to guide researchers and practitioners in conducting effective empirical user-centered evaluations by analyzing several studies; categorizing their objectives, scope, and evaluation metrics; and offering an orientation map for research design and metric measurement.Source Type:Article5 - Some of the metrics are blocked by yourconsent settings
Publication Access status: Open Access , Explaining AI Decisions: Towards Achieving Human-Centered Explainability in Smart Home EnvironmentsSmart home systems are gaining popularity as homeowners strive to enhance their living and working environments while minimizing energy consumption. However, the adoption of artificial intelligence (AI)-enabled decision-making models in smart home systems faces challenges due to the complexity and black-box nature of these systems, leading to concerns about explainability, trust, transparency, accountability, and fairness. The emerging field of explainable artificial intelligence (XAI) addresses these issues by providing explanations for the models’ decisions and actions. While state-of-the-art XAI methods are beneficial for AI developers and practitioners, they may not be easily understood by general users, particularly household members. This paper advocates for human-centered XAI methods, emphasizing the importance of delivering readily comprehensible explanations to enhance user satisfaction and drive the adoption of smart home systems. We review state-of-the-art XAI methods and prior studies focusing on human-centered explanations for general users in the context of smart home applications. Through experiments on two smart home application scenarios, we demonstrate that explanations generated by prominent XAI techniques might not be effective in helping users understand and make decisions. We thus argue for the necessity of a human-centric approach in representing explanations in smart home systems and highlight relevant human-computer interaction (HCI) methodologies, including user studies, prototyping, technology probes analysis, and heuristic evaluation, that can be employed to generate and present human-centered explanations to users.Source Type:InProceedings9 33 - Some of the metrics are blocked by yourconsent settings
Publication Access status: Metadata only , From Surplus and Scarcity toward Abundance: Understanding the Use of ICT in Food Resource Sharing PracticesFood practices have become an important context for questions around sustainability. Within HCI, sustainable HCI and human-food-interaction have developed as a response. We argue, nevertheless, that food practices as a social activity remain relatively under-examined, and further that sustainable food practices hinge on communal activity. We present the results of action-oriented research with a grassroots movement committed to sustainable food practices at a local, communal level, thereby demonstrating the role of ICT in making food resource sharing a viable practice. We suggest that the current focus on food sharing might usefully be supplemented by attention to food resource sharing, an approach that aligns with a paradigm shift from surplus to abundance. We argue for a design that aims to encourage food resource sharing at a local level but that also has wider ramifications. These “glocal” endeavors recognize the complexity of prosumption practices and foster aspirations for “deep change” in food systems.Source Type:ArticleDOI:10.1145/35899571 - Some of the metrics are blocked by yourconsent settings
Publication Access status: Open Access , Towards user-centered explainable energy demand forecasting systems(2022); ; In recent years, eXplainable Artificial Intelligence (XAI) has received huge attention in the area of explaining the decision-making processes of machine learning models. The aim is to increase the acceptance, trust, and transparency of AI models by providing explanations about the models' decisions. But most of the prior works on XAI are focused to support AI practitioners and developers in understanding and debugging. In this paper, we propose a user-centered explainable energy demand prediction and forecasting system that aims to provide explanations to end-users in the smart home. In doing so, we present an overview of the explainable system and propose a method combining Deep Learning Important FeaTures (DeepLIFT) and Shapley Additive Explanations (SHAP) to explain the prediction of an LSTM-based energy forecasting model.Source Type:InProceedings4 33