  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.
