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.

智能家居系统中智能决策模型的可解释人工智能（XAI）方法对于开发者和从业者有益，但普通用户尤其是家庭成员可能很难理解。本文提倡以人为中心的 XAI 方法，强调提供易于理解的解释以增强用户满意度、推动智能家居系统的采用。通过对两个智能家居应用场景的实验，我们证明著名的 XAI 技术生成的解释可能无法帮助用户理解和做出决策，因此我们主张在智能家居系统中采用以人为中心的方法来表达解释，并介绍相关的人机交互（HCI）方法，包括用户研究、原型设计、技术探针分析和启发式评估，以为用户生成和展示人为中心的解释。

智能家居环境中实现以人为中心的可解释性：解释 AI 决策

Explaining AI Decisions: Towards Achieving Human-Centered Explainability  in Smart Home Environments

We introduce and evaluate an eXplainable Goal Recognition (XGR) model that
uses the Weight of Evidence (WoE) framework to explain goal recognition
problems. Our model provides human-centered explanations that answer why? and
why not? questions. We computationally evaluate the performance of our system
over eight different domains. Using a human behavioral study to obtain the
ground truth from human annotators, we further show that the XGR model can
successfully generate human-like explanations. We then report on a study with
60 participants who observe agents playing Sokoban game and then receive
explanations of the goal recognition output. We investigate participants'
understanding obtained by explanations through task prediction, explanation
satisfaction, and trust.

该研究使用证据权值框架介绍和评估可解释性目标识别模型，提供人性化的解释并研究了不同场景下其表现，并通过人类行为研究和 Sokoban 游戏研究证明该模型可以成功生成人类模拟的解释，并调查观察者在接受解释后对任务预测、解释满意度和信任的理解。