Trust in foundation models and GenAI: A geographic perspective

TL;DR

This paper explores the multifaceted concept of trust in foundation models and GenAI within geography, emphasizing cultural, ethical, and operational aspects to guide responsible development and application.

Contribution

It introduces a comprehensive framework categorizing trust into epistemic, operational, and interpersonal types specific to geographic applications.

Findings

Identifies key trust categories relevant to geographic AI.

Highlights importance of transparency, bias mitigation, and regional policies.

Discusses challenges related to data heterogeneity and biases.

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.