Philosophical Foundations of GeoAI: Exploring Sustainability, Diversity, and Bias in GeoAI and Spatial Data Science

TL;DR

This paper explores the philosophical foundations of GeoAI, emphasizing ethical considerations like sustainability, bias, and diversity to guide responsible research and deployment of spatial AI systems.

Contribution

It introduces a philosophical perspective on GeoAI, focusing on ethical principles such as sustainability, bias mitigation, and diversity, which are often overlooked in technical discussions.

Findings

Highlights ethical implications of GeoAI systems

Identifies potential biases and lack of neutrality in GeoAI

Proposes a unifying ethical framework for spatial data science

Abstract

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.