  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.

Computers and Society;Artificial Intelligence;Machine Learning