Trust in foundation models and GenAI: A geographic perspective
Grant McKenzie, Krzysztof Janowicz, Carsten Kessler
TL;DR
The chapter investigates trust in foundation models and GenAI from a geographic perspective, decomposing trust into epistemic (data), operational (model), and interpersonal (modeler) facets and exploring how spatial context, data heterogeneity, and cultural factors shape trust. It analyzes data quality, biases, transparency, explainability, and ethics as core ingredients for credible GeoAI, and discusses security, risk, and user adoption implications in geographic applications. The geographic lens highlights regional data disparities, privacy concerns, and digital sovereignty, arguing for regionally-informed policies and community engagement to mitigate bias and enhance transparency. Together, the work offers a conceptual starting point for researchers, practitioners, and policymakers to foster trustworthy, regionally-aware GeoAI deployments with robust transparency, bias mitigation, and uncertainty communication.
Abstract
Large-scale pre-trained machine learning models have reshaped our understanding of artificial intelligence across numerous domains, including our own field of geography. As with any new technology, trust has taken on an important role in this discussion. In this chapter, we examine the multifaceted concept of trust in foundation models, particularly within a geographic context. As reliance on these models increases and they become relied upon for critical decision-making, trust, while essential, has become a fractured concept. Here we categorize trust into three types: epistemic trust in the training data, operational trust in the model's functionality, and interpersonal trust in the model developers. Each type of trust brings with it unique implications for geographic applications. Topics such as cultural context, data heterogeneity, and spatial relationships are fundamental to the spatial sciences and play an important role in developing trust. The chapter continues with a discussion of the challenges posed by different forms of biases, the importance of transparency and explainability, and ethical responsibilities in model development. Finally, the novel perspective of geographic information scientists is emphasized with a call for further transparency, bias mitigation, and regionally-informed policies. Simply put, this chapter aims to provide a conceptual starting point for researchers, practitioners, and policy-makers to better understand trust in (generative) GeoAI.