Fallibilism and Generative AI in Cartography: Some Fundamental Theoretical Thoughts

Abstract This article explores the significance of various forms of fallibilism in the context of generative artificial intelligence (AI) and its applications in cartography. Fallibilism, as an epistemological approach, emphasizes the fundamental fallibility of knowledge (here particularly scientific knowledge and AI-generated knowledge) and calls for critical reflection on its limits and uncertainties. Five variants of fallibilism (epistemological, methodological, ontological, pragmatic, and neopragmatic) are examined in this context. The epistemological approach emphasizes the provisional nature of knowledge, while the methodological approach focuses on the need for error-tolerant methods. Ontological fallibilism questions fundamental assumptions about reality, and pragmatic and neopragmatic fallibilism emphasize the practical utility of knowledge and iterative development. The neopragmatic approach, which integrates all other perspectives, offers a flexible and practice-oriented framework. This framework promotes the creation of useful, dynamic, and inclusive cartographic applications. The article discusses how generative AI can be utilized within the neopragmatic framework of fallibilism to constructively address uncertainties and develop socially relevant solutions, particularly in the realm of cartography.