随着气候极端事件与灾害日益频发且强度加剧，地理空间人工智能（GeoAI）已成为支撑大规模灾害制图与风险减缓的变革性技术。然而，若仅以机械式、性能导向的方式部署GeoAI模型，则可能加剧固有的空间不平等、阻碍有效的应急决策，并产生显著的环境碳足迹。为系统解构‘负责任的GeoAI’这一概念，本文从批判性地理信息系统（Critical GIS）视角出发，考察其在气候极端事件与灾害制图等新兴应用中的角色。我们从四个相互关联的理论维度——代表性（Representativeness）、可解释性（Explainability）、可持续性（Sustainability）与伦理（Ethics）——阐释负责任GeoAI的内涵，并辅以气候极端事件与灾害制图领域的实例。此外，面向实际操作层面，我们进一步提出一个负责任GeoAI的概念化治理模型，将其治理实践划分为数据（Data）、应用（Application）与社会（Society）三个范畴。最后，本文旨在唤起更广泛的GIS学界关注：气候韧性建设的未来不仅依赖于开发更优算法，更取决于构建一个使GeoAI得以负责任、合伦理且可持续部署的治理生态系统。

As climate extreme and disaster events become more frequent and intense, Geospatial Artificial Intelligence (GeoAI) has emerged as a transformative approach for large-scale disaster mapping and risk reduction. However, the purely mechanical, performance-driven deployment of GeoAI models can result in amplifying inherent spatial inequalities, preventing effective emergency decision-making, and producing severe environmental carbon footprint. To unbox the concept of responsible GeoAI, this position paper examines its emerging role, e.g., in climate extreme and disaster mapping, from a critical GIS perspective. We address the nexus of responsible GeoAI into four interrelated theoretical dimensions, specifically Representativeness, Explainability, Sustainability, and Ethics, with examples from climate extreme and disaster mapping. Moreover, targeting at the operational practice, we then propose a conceptual governance Model of responsible GeoAI that categorizes its governance practices into Data, Application, and Society scopes. Last, this position paper aims to raise the attention in the broader GIS community that the future of climate resilience relies not just on building better algorithms, but on fostering a governance ecosystem where GeoAI is deployed responsibly, ethically, and sustainably.