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.

本文探讨了 GeoAI 和空间数据科学的哲学基础，重点强调了可持续性、培训数据中的偏见、模式知识的多样性和 GeoAI 系统的中立性（潜在缺乏）等主题，并从统一的伦理学角度出发思考我们专业的伦理影响，帮助我们更负责任地开展潜在的颠覆性研究，识别设计、培训和部署基于 GeoAI 的系统的陷阱，并在学术领域内共同开发人工智能和机器学习研究的优势和潜在风险，同时与他人分享我们独特的（地球）空间视角。