从社区价值到AI设计约束：一项关于洛杉矶县拟议AI驱动野火风险评估工具的混合方法研究
From Community Values to AI Design Constraints: A Mixed-Methods Study of a Proposed AI-Driven Wildfire Risk Assessment Tool in Los Angeles County
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中文总结 AI 辅助
本研究通过混合方法调查洛杉矶县居民对拟议AI野火风险评估工具的价值与关切，提出AI价值地图，将社区意见转化为可追溯的设计约束。
中文摘要 AI 辅助
面向居民的AI驱动灾害工具越来越多地被提议用于风险沟通。然而，居民往往在数据使用、隐私和解释方面的决策已经做出之后才被征求反馈。本研究采取了一种更早的方法，在预测模型或功能界面开发之前，考察居民对拟议的基于AI的风险评估工具的价值、关切和实际期望。拟议的智能手机应用程序将使用房产照片来估计地块级别的野火风险，并提供可解释的风险评分、不确定性信息和缓解建议。采用收敛式混合方法设计，我们调查了30名洛杉矶县居民，其中7人还参加了虚拟市政厅会议。我们使用描述性统计、探索性FDR调整的Spearman相关性分析以及开放式和市政厅回答的归纳主题编码对数据进行了分析。参与者持谨慎接受态度：66.7%的人表示他们很可能或可能会使用该工具。他们将公平的风险评估与工具是否考虑了相关的房产和社区条件联系起来。隐私和数据安全是最常见的关切（64.3%），而成本是采取建议行动的主要障碍（65.5%）。使用工具的可能性与保存或分享风险结果的可能性强烈相关（$\rho = 0.752$，$p_{\mathrm{FDR}} < .001$）。我们将研究结果转化为AI价值地图，这是一个具有九个价值维度的社区基础工件。该地图将参与者的发现与临时设计要求和如果这些要求未被解决可能产生的后果联系起来。本研究提供了关于居民对拟议AI野火工具反应的探索性证据，并提供了一种预模型程序，用于将早期社区意见转化为面向居民的AI灾害工具的可追溯设计约束。
英文摘要
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.
发表机构
- University of North Carolina at Charlotte（北卡罗来纳大学夏洛特分校）
- California State University, Los Angeles（洛杉矶加州州立大学）
机构由 AI 辅助整理，请以论文原文为准。
