GeoAI的哲学基础：探索GeoAI与空间数据科学中的可持续性、多样性与偏见
Philosophical Foundations of GeoAI: Exploring Sustainability, Diversity, and Bias in GeoAI and Spatial Data Science
AI总结：
本章从统一伦理视角提出GeoAI与空间数据科学的哲学基础，聚焦可持续性、训练数据偏见、模式知识多样性及系统中立性缺失问题，以促进负责任的研究与系统部署。
摘要：
本章阐述了可构成GeoAI与空间数据科学哲学基础的一些基本假设和原则。本章并未回顾空间数据（分析）中已被充分确立的特征，包括交互、邻域和自相关，而是从统一的伦理视角出发，重点探讨了可持续性、训练数据中的偏见、模式知识的多样性以及GeoAI系统（可能缺乏的）中立性等主题。反思我们职业的伦理意涵，将有助于我们更负责任地开展具有潜在颠覆性的研究，识别在设计、训练和部署基于GeoAI的系统时的隐患，并就人工智能与机器学习研究跨学术领域的益处及潜在危险形成共同理解，同时与他人分享我们独特的（地理）空间视角。
英文摘要：
This chapter presents some of the fundamental assumptions and principles that could form the philosophical foundation of GeoAI and spatial data science. Instead of reviewing the well-established characteristics of spatial data (analysis), including interaction, neighborhoods, and autocorrelation, the chapter highlights themes such as sustainability, bias in training data, diversity in schema knowledge, and the (potential lack of) neutrality of GeoAI systems from a unifying ethical perspective. Reflecting on our profession's ethical implications will assist us in conducting potentially disruptive research more responsibly, identifying pitfalls in designing, training, and deploying GeoAI-based systems, and developing a shared understanding of the benefits but also potential dangers of artificial intelligence and machine learning research across academic fields, all while sharing our unique (geo)spatial perspective with others.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
