Climate-driven wildfires are intensifying, particularly in urban regions such as Southern California. Yet, traditional fire risk communication tools often fail to gain public trust due to inaccessible design, non-transparent outputs, and limited contextual relevance. These challenges are especially critical in high-risk communities, where trust depends on how clearly and locally information is presented. Neighborhoods such as Pacific Palisades, Pasadena, and Altadena in Los Angeles exemplify these conditions. This study introduces a community-led approach for integrating AI into wildfire risk assessment using the Participatory AI Literacy and Explainability Integration (PALEI) framework. PALEI emphasizes early literacy building, value alignment, and participatory evaluation before deploying predictive models, prioritizing clarity, accessibility, and mutual learning between developers and residents. Early engagement findings show strong acceptance of visual, context-specific risk communication, positive fairness perceptions, and clear adoption interest, alongside privacy and data security concerns that influence trust. Participants emphasized localized imagery, accessible explanations, neighborhood-specific mitigation guidance, and transparent communication of uncertainty. The outcome is a mobile application co-designed with users and stakeholders, enabling residents to scan visible property features and receive interpretable fire risk scores with tailored recommendations. By embedding local context into design, the tool becomes an everyday resource for risk awareness and preparedness. This study argues that user experience is central to ethical and effective AI deployment and provides a replicable, literacy-first pathway for applying the PALEI framework to climate-related hazards.

翻译：气候驱动的野火日益加剧，尤其是在南加州等城市区域。然而，由于设计不可及、输出不透明以及情境相关性有限，传统的火灾风险沟通工具往往难以获得公众信任。这些挑战在高风险社区尤为关键，因为信任取决于信息呈现的清晰程度和本地化程度。洛杉矶的太平洋帕利塞兹、帕萨迪纳和阿尔塔迪纳等社区是这些条件的典型例证。本研究引入了一种社区主导的方法，利用参与式AI素养与可解释性整合（PALEI）框架将AI整合到野火风险评估中。PALEI强调在部署预测模型之前，建立早期素养、实现价值对齐并进行参与式评估，优先考虑清晰性、可及性以及开发者和居民之间的相互学习。初步参与结果表明，视觉化、情境特定的风险沟通被广泛接受，公平性认知良好，采纳意愿明确，同时隐私和数据安全问题也影响了信任。参与者强调本地化图像、可及的解释、针对特定社区的缓解指南以及不确定性信息的透明沟通。最终成果是一款与用户和利益相关者共同设计的移动应用程序，使居民能够扫描可见的房产特征，并获得可解释的火险评分及个性化建议。通过将本地情境嵌入设计，该工具成为风险意识和备灾的日常资源。本研究认为，用户体验是合乎伦理且有效部署AI的核心，并为将PALEI框架应用于气候相关灾害提供了一条可复制的、以素养优先的路径。