人工智能在应急管理中的透明度:建立信任和问责制

Jaideep Visave
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引用次数: 0

摘要

人工智能(AI)站在变革应急管理的最前沿,在备灾和救灾方面提供了前所未有的能力。最近的实践证明了这种从被动到主动的转变,特别是通过洪水预测算法和海上搜救优化系统,整合了实时船舶位置和天气数据。然而,目前的形势揭示了一个关键的挑战:人工智能系统的不透明性在应急响应人员和社区之间造成了严重的信任赤字。研究结果描绘了这一透明度差距的令人担忧的画面。对应急管理人工智能系统的全面调查揭示了惊人的统计数据:68%的人缺乏足够的数据来源文件,而42%的人未能为其建议提供明确的理由。这种“黑箱”现象具有严重的影响,特别是当洪水预测模型不成比例地影响弱势群体时,或者当不透明的决策过程导致关键救援行动中的资源分配不理想时。对洪水准备和搜救行动中的实际应用进行的分析揭示了这些基本应急响应框架中存在的系统性沟通缺陷。该研究考察了不同程度的人工智能透明度如何直接影响危机期间应急响应人员的决策,探索了操作开放性和安全考虑之间的微妙平衡。这些发现突出表明,迫切需要强有力的监督机制和针对具体情况的透明度协议,以确保在应急管理中道德地部署人工智能。证据指向一个明确的解决办法:发展以人为本的方法,增强而不是取代应急响应中的人类能力。这一战略需要建立量身定制的透明度指导方针和监测系统,以应对当前的挑战,同时促进有效的人工智能整合。通过优先考虑技术进步和人为监督,应急管理系统可以更好地履行其关键的公共安全使命。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Transparency in AI for emergency management: building trust and accountability

Artificial intelligence (AI) stands at the forefront of transforming emergency management, offering unprecedented capabilities in disaster preparedness and response. Recent implementations demonstrate this shift from reactive to proactive approaches, particularly through flood prediction algorithms and maritime search-and-rescue optimization systems that integrate real-time vessel locations and weather data. However, the current landscape reveals a critical challenge: the opacity of AI systems creates a significant trust deficit among emergency responders and communities. Research findings paint a concerning picture of this transparency gap. A comprehensive survey of emergency management AI systems reveals striking statistics: 68% lack adequate documentation of their data sources, while 42% fail to provide clear justifications for their recommendations. This “black box” phenomenon carries serious implications, particularly when flood prediction models disproportionately affect vulnerable populations or when opaque decision-making processes lead to suboptimal resource allocation during critical rescue operations. Analysis of real-world applications in flood preparedness and search-and-rescue operations exposes systematic communication deficiencies within these essential emergency response frameworks. The research examines how varying levels of AI transparency directly influence emergency responders' decision-making during crises, exploring the delicate balance between operational openness and security considerations. These findings highlight an urgent need for robust oversight mechanisms and context-specific transparency protocols to ensure ethical AI deployment in emergency management. The evidence points toward a clear solution: developing human-centric approaches that enhance rather than replace human capabilities in emergency response. This strategy requires establishing tailored transparency guidelines and monitoring systems that address current challenges while facilitating effective AI integration. By prioritizing both technological advancement and human oversight, emergency management systems can better serve their critical public safety mission.

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