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The Ethics of AI-Generated Maps: A Study of DALLE 2 and Implications for Cartography

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arxiv 2304.10743 v3 pith:V72ZBO3D submitted 2023-04-21 cs.CY cs.HC

classification cs.CYcs.HC
keywords mapsethicalai-generateddallecartographyartificialconcernsdevelopment
verification ladder T0 review T1 audit T2 compute T3 formal

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The rapid advancement of artificial intelligence (AI) such as the emergence of large language models including ChatGPT and DALLE 2 has brought both opportunities for improving productivity and raised ethical concerns. This paper investigates the ethics of using artificial intelligence (AI) in cartography, with a particular focus on the generation of maps using DALLE 2. To accomplish this, we first create an open-sourced dataset that includes synthetic (AI-generated) and real-world (human-designed) maps at multiple scales with a variety settings. We subsequently examine four potential ethical concerns that may arise from the characteristics of DALLE 2 generated maps, namely inaccuracies, misleading information, unanticipated features, and reproducibility. We then develop a deep learning-based ethical examination system that identifies those AI-generated maps. Our research emphasizes the importance of ethical considerations in the development and use of AI techniques in cartography, contributing to the growing body of work on trustworthy maps. We aim to raise public awareness of the potential risks associated with AI-generated maps and support the development of ethical guidelines for their future use.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. CartoAgent: a multimodal large language model-powered multi-agent cartographic framework for map style transfer and evaluation

    cs.HC 2025-05 conditional novelty 6.0 of 10

    A multi-agent multimodal-LLM framework performs map style transfer by iteratively designing stylesheets and reviewing maps, with expert raters agreeing with its final map choices in 83.82 percent of trials.

  2. Envisioning Generative Artificial Intelligence in Cartography and Mapmaking

    cs.HC 2025-08 unverdicted novelty 4.0 of 10

    The authors argue that generative AI can support many cartographic tasks, such as symbolization, map evaluation, and map reading, while flagging precision-critical tasks and ethical risks as unsuitable for now.

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