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.

翻译：随着极端气候与灾害事件日益频繁和剧烈，地理空间人工智能已成为大规模灾害制图与减灾的一种变革性方法。然而，纯粹以机械性和性能为导向部署地理空间人工智能模型，可能会放大固有的空间不平等性、阻碍有效的应急决策，并产生严重的环境碳足迹。为了剖析责任地理空间人工智能这一概念，本文从批判地理信息科学视角出发，探讨其在新兴领域（例如极端气候与灾害制图）中的作用。我们从四个相互关联的理论维度（即代表性、可解释性、可持续性与伦理）阐述责任地理空间人工智能的核心，并结合极端气候与灾害制图实例加以说明。此外，针对运行实践，我们提出一个概念性的责任地理空间人工智能治理模型，将治理实践划分为数据、应用与社会三个范畴。最后，本文旨在引起更广泛地理信息科学界的关注：气候韧性的未来不仅依赖于构建更优的算法，更在于培育一个以负责任、合乎伦理且可持续的方式部署地理空间人工智能的治理生态系统。