Trust in foundation models and GenAI: A geographic perspective

2025-10-20

AI Summary

This paper addresses the trust deficit in generative Geospatial Artificial Intelligence (GeoAI) by proposing a three-dimensional trust framework grounded in geographic context: cognitive trust (assessing regional representativeness and cultural adaptability of training data), operational trust (evaluating spatial interpretability and robustness of model functionalities), and interpersonal trust (clarifying developer accountability and multi-stakeholder governance mechanisms). Drawing on geoinformation science theory, explainable AI (XAI) techniques, and ethical governance frameworks, the study conducts conceptual analysis and interdisciplinary inquiry, emphasizing spatial heterogeneity, alignment with regional policy contexts, and dynamic bias mitigation. Its key contribution is the first systematic articulation of a geography-sensitive trust paradigm for GeoAI—explicitly positioning geoinformation scientists as pivotal actors in AI governance—and delivering a theoretically rigorous yet practice-oriented roadmap for trust assessment and cultivation, tailored for researchers, practitioners, and policymakers.

Technology Category

• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Computer Vision: Remote Sensing / Geospatial AI

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Semantics and Knowledge: Provenance, trust, security and privacy, and ethical issues in managing semantic data
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

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.

Problem

• Analyzing trust dimensions in foundation models for geography
• Addressing data and operational biases in geographic AI systems
• Developing transparent GeoAI frameworks for spatial decision-making

Innovation

• Categorizing trust into three distinct types
• Emphasizing transparency and bias mitigation strategies
• Proposing regionally-informed policies for GeoAI