The Ethics of AI-Generated Maps: DALL·E 2 and AI’s Implications for Cartography (Short Paper)

Authors Qianheng Zhang, Yuhao Kang , Robert Roth



PDF
Thumbnail PDF

File

LIPIcs.GIScience.2023.93.pdf
  • Filesize: 2.34 MB
  • 6 pages

Document Identifiers

Author Details

Qianheng Zhang
  • HGIS Lab, Department of Geography, University of Washington, Seattle, WA, USA
Yuhao Kang
  • GeoDS Lab, Department of Geography, University of Wisconsin-Madison, WI, USA
Robert Roth
  • Cartography Lab, Department of Geography, University of Wisconsin-Madison, WI, USA

Acknowledgements

The authors would like to express their sincere gratitude for the support received from Dr. Song Gao at the GeoDS Lab, University of Wisconsin-Madison, Dr. Bo Zhao, and Yifan Sun at the HGIS Lab, University of Washington.

Cite As Get BibTex

Qianheng Zhang, Yuhao Kang, and Robert Roth. The Ethics of AI-Generated Maps: DALL·E 2 and AI’s Implications for Cartography (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 93:1-93:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023) https://doi.org/10.4230/LIPIcs.GIScience.2023.93

Abstract

The rapid advancement of artificial intelligence (AI) such as the emergence of large language models ChatGPT and DALL·E 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 DALL·E 2. To accomplish this, we first created an open-sourced dataset that includes synthetic (AI-generated) and real-world (human-designed) maps at multiple scales with a variety of settings. We subsequently examined four potential ethical concerns that may arise from the characteristics of DALL·E 2 generated maps, namely inaccuracies, misleading information, unanticipated features, and irreproducibility. We then developed a deep learning-based model to identify 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.

Subject Classification

ACM Subject Classification
  • Human-centered computing → Human computer interaction (HCI)
Keywords
  • Ethics
  • GeoAI
  • DALL-E
  • Cartography

Metrics

  • Access Statistics
  • Total Accesses (updated on a weekly basis)
    0
    PDF Downloads

References

  1. Robert Chambers. Participatory mapping and geographic information systems: whose map? who is empowered and who disempowered? who gains and who loses? The Electronic Journal of Information Systems in Developing Countries, 25(1):1-11, 2006. Google Scholar
  2. Taisheng Chen, Menglin Chen, A-Xing Zhu, and Weixing Jiang. A learning-based approach to automatically evaluate the quality of sequential color schemes for maps. Cartography and Geographic Information Science, 48(5):377-392, 2021. Google Scholar
  3. Sidonie Christophe, Samuel Mermet, Morgan Laurent, and Guillaume Touya. Neural map style transfer exploration with gans. International Journal of Cartography, 8(1):18-36, 2022. Google Scholar
  4. Azelle Courtial, Guillaume Touya, and Xiang Zhang. Deriving map images of generalised mountain roads with generative adversarial networks. International Journal of Geographical Information Science, 37(3):499-528, 2023. Google Scholar
  5. Michael R Evans, Ahmad Mahmoody, Dragomir Yankov, Florin Teodorescu, Wei Wu, and Pavel Berkhin. Livemaps: Learning geo-intent from images of maps on a large scale. In Proceedings of the 25th ACM SIGSPATIAL international conference on advances in geographic information systems, pages 1-9, 2017. Google Scholar
  6. Amy L Griffin. Trustworthy maps. Journal of Spatial Information Science, 2020(20):5-19, 2020. Google Scholar
  7. Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016. Google Scholar
  8. Yingjie Hu, Zhipeng Gui, Jimin Wang, and Muxian Li. Enriching the metadata of map images: a deep learning approach with gis-based data augmentation. International Journal of Geographical Information Science, 36(4):799-821, 2022. Google Scholar
  9. Yuhao Kang, Song Gao, and Robert Roth. A review and synthesis of recent geoai research for cartography: Methods, applications, and ethics. In AutoCarto 2022, 2022. Google Scholar
  10. Yuhao Kang, Song Gao, and Robert E Roth. Transferring multiscale map styles using generative adversarial networks. International Journal of Cartography, 5(2-3):115-141, 2019. Google Scholar
  11. Yuhao Kang, Qianheng Zhang, and Robert Roth. The ethics of ai-generated maps: A study of dalle 2 and implications for cartography. arXiv preprint arXiv:2304.10743, 2023. Google Scholar
  12. Gengchen Mai, Weiming Huang, Jin Sun, Suhang Song, Deepak Mishra, Ninghao Liu, Song Gao, Tianming Liu, Gao Cong, Yingjie Hu, Chris Cundy, Ziyuan Li, Rui Zhu, and Ni Lao. On the opportunities and challenges of foundation models for geospatial artificial intelligence, 2023. URL: https://arxiv.org/abs/2304.06798.
  13. Scott McLean, Gemma JM Read, Jason Thompson, Chris Baber, Neville A Stanton, and Paul M Salmon. The risks associated with artificial general intelligence: A systematic review. Journal of Experimental & Theoretical Artificial Intelligence, pages 1-15, 2021. Google Scholar
  14. David Mhlanga. Open ai in education, the responsible and ethical use of chatgpt towards lifelong learning. Education, the Responsible and Ethical Use of ChatGPT Towards Lifelong Learning (February 11, 2023), 2023. Google Scholar
  15. Mark Monmonier. How to lie with maps. University of Chicago Press, 2018. Google Scholar
  16. OpenAI. Gpt-4 technical report, 2023. URL: https://arxiv.org/abs/2303.08774.
  17. Anthony C Robinson, Pyry Kettunen, Luciene Delazari, and Arzu Çöltekin. New directions for the state of the art and science in cartography. International Journal of Cartography, pages 1-7, 2023. Google Scholar
  18. Yilang Shen, Tinghua Ai, and Rong Zhao. Raster-based method for building selection in the multi-scale representation of two-dimensional maps. Geocarto International, 37(22):6494-6518, 2022. Google Scholar
  19. Eva AM van Dis, Johan Bollen, Willem Zuidema, Robert van Rooij, and Claudi L Bockting. Chatgpt: five priorities for research. Nature, 614(7947):224-226, 2023. Google Scholar
  20. Ali Zarifhonarvar. Economics of chatgpt: A labor market view on the occupational impact of artificial intelligence. Available at SSRN 4350925, 2023. Google Scholar
  21. Bo Zhao, Shaozeng Zhang, Chunxue Xu, Yifan Sun, and Chengbin Deng. Deep fake geography? when geospatial data encounter artificial intelligence. Cartography and Geographic Information Science, 48(4):338-352, 2021. Google Scholar
  22. Terry Yue Zhuo, Yujin Huang, Chunyang Chen, and Zhenchang Xing. Exploring ai ethics of chatgpt: A diagnostic analysis. arXiv preprint arXiv:2301.12867, 2023. Google Scholar
Questions / Remarks / Feedback
X

Feedback for Dagstuhl Publishing


Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail