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arXiv · 2610.03724

From Community Values to AI Design Constraints: A Mixed-Methods Study of a Proposed AI-Driven Wildfire Risk Assessment Tool in Los Angeles County

Sanaz Sadat Hosseini·Mona Azarbayjani·Mohammad Pourhomayoun·Hamed Tabkhi
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Abstract

AI-driven hazard tools are increasingly proposed for resident-facing risk communication. However, residents are often asked for feedback only after decisions about data use, privacy, and explanation have been made. This study takes an earlier approach by examining residents' values, concerns, and practical expectations for a proposed AI-based risk assessment tool before a predictive model or functional interface is developed. The proposed smartphone application would use property photographs to estimate parcel-level wildfire risk and provide an interpretable risk score, uncertainty information, and mitigation recommendations. Using a convergent mixed-methods design, we surveyed 30 Los Angeles County residents, seven of whom also participated in a virtual town hall. We analyzed the data using descriptive statistics, exploratory FDR-adjusted Spearman correlations, and inductive thematic coding of open-ended and town hall responses. Participants were cautiously receptive: 66.7% said they would be very likely or likely to use the tool. They linked fair risk assessment to whether the tool considered relevant property and neighborhood conditions. Privacy and data security were the most common concerns (64.3%), while cost was the main barrier to acting on recommendations (65.5%). Likelihood of using the tool was strongly associated with likelihood of saving or sharing risk results ($ρ= 0.752$, $p_{\mathrm{FDR}} < .001$). We translated the findings into the AI Value Map, a community-grounded artifact with nine value dimensions. The Map connects participant findings to provisional design requirements and possible consequences if they are not addressed. This study provides exploratory evidence on residents' responses to a proposed AI wildfire tool and offers a pre-model procedure for translating early community input into traceable design constraints for resident-facing AI hazard tools.

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Sanaz Sadat Hosseini, Mona Azarbayjani, Mohammad Pourhomayoun, Hamed Tabkhi. 2026-08-12. From Community Values to AI Design Constraints: A Mixed-Methods Study of a Proposed AI-Driven Wildfire Risk Assessment Tool in Los Angeles County. https://arxiv.org/abs/2610.03724

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