Community-Led AI Integration for Wildfire Risk Assessment: A Participatory AI Literacy and Explainability Integration (PALEI) Framework in Los Angeles, CA

2026-04-19

AI Summary

Traditional wildfire risk assessment tools often fail to gain public trust in high-risk communities due to limited transparency and insufficient local relevance. This study proposes the Participatory AI Literacy and Explainability Integration (PALEI) framework, introducing a novel “literacy-first, co-designed with community” paradigm that embeds local context deeply into system design prior to model deployment. By fostering early AI literacy, aligning values, and enabling joint evaluation, the approach ensures community engagement from the outset. Integrating mobile-based image recognition, explainable AI, participatory design, and uncertainty visualization, the project developed a mobile application allowing residents to scan their home features and receive personalized risk scores and mitigation recommendations. This significantly enhances public acceptance, perceived fairness, and willingness to use wildfire risk communication tools.

Technology Category

• Humans and AI: Explainable AI (XAI) for Human Understanding
• Computer Vision: Remote Sensing / Geospatial AI
• Philosophy and Ethics of AI: Accountability, Interpretability & Explainability

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

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.

Problem

• wildfire risk assessment
• public trust
• AI explainability
• community engagement
• risk communication

Innovation

• Participatory AI
• Explainable AI
• AI Literacy
• Community-led Design
• Wildfire Risk Assessment