Stevens, Gunnar

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Stevens, Gunnar
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    PublicationAccess status: Open Access ,
    Explaining AI Decisions: Towards Achieving Human-Centered Explainability in Smart Home Environments
    (2024)
    Shajalal, Md
    ;
    Boden, Alexander
    ;
    Stevens, Gunnar
    ;
    Du, Delong
    ;
    Kern, Dean-Robin
    Smart 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:InProceedings
    DOI:10.25819/ubsi/10831
      9  33
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    PublicationAccess status: Open Access ,
    Towards user-centered explainable energy demand forecasting systems
    (2022)
    Shajalal, Md
    ;
    Boden, Alexander
    ;
    Stevens, Gunnar
    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:InProceedings
    DOI:10.25819/ubsi/10827
      4  33