Imagining the algorithmic city: bias, cultural authenticity, and spatial futures in AI-generated representations of Indonesian urban environments

Fadilah Wasiqah

doi:10.67490/ijls.v1i2.897

Authors

Fadilah Wasiqah Universitas Terbuka Yogyakarta Author

DOI:

https://doi.org/10.67490/ijls.v1i2.897

Keywords:

algorithmic bias, cultural authenticity, generative AI, Indonesian cities, spatial futures

Abstract

Background: Generative artificial intelligence increasingly mediates how Indonesian cities are visualized, yet its synthetic urban imagery may privilege iconic infrastructure and global metropolitan aesthetics while weakening social, linguistic, and cultural complexity. Objective: This study examines how algorithmic bias, cultural authenticity, and spatial futurity are constructed across AI-generated representations of Jakarta, Surabaya–Madura, Bandung, Yogyakarta, and national urban futures. Method: A comparative qualitative multimodal design analyzes twelve static AI-generated images, supported by secondary scholarship and official contextual documents, through spatial representation auditing, a cultural-authenticity and semiotic-specificity matrix, and spatial-futures narrative analysis. Results: Findings show that skylines, monuments, bridges, and engineered environments provide strong geographical recognizability, whereas informal spaces and socially differentiated actors remain comparatively marginal. Cultural identity is retained primarily through landmarks and religious architecture, but vernacular forms, linguistic signage, and everyday practices receive substantially less visibility. Implication: Future orientation is expressed mainly through technological order, polished infrastructure, and globally legible urban form, while ecological complexity and social inclusion remain limited. Novelty: This study contributes an integrated analytical framework demonstrating that representational bias may operate through selective preservation rather than complete cultural erasure, thereby connecting urban visual analysis, cultural semiotics, and critical AI urbanism within a comparative Indonesian context and offering a transferable approach for evaluating synthetic cities.

References

[1] S. Akter, Y. K. Dwivedi, S. Sajib, K. Biswas, R. Bandara, and K. Michael, “Algorithmic bias in machine learning-based marketing models,” Journal of Business Research, 2022, doi: 10.1016/j.jbusres.2022.01.083.

[2] C. Hou, F. Zhang, Y. Kang, S. Gao, Y. Li, F. Duarte, and S. Li, “Transferred bias uncovers the balance between the development of physical and socioeconomic environments of cities,” Annals of the American Association of Geographers, vol. 115, pp. 148–166, 2024, doi: 10.1080/24694452.2024.2412173.

[3] P. Liu, Y. Zhang, and F. Biljecki, “Explainable spatially explicit geospatial artificial intelligence in urban analytics,” Environment and Planning B: Urban Analytics and City Science, vol. 51, pp. 1104–1123, 2023, doi: 10.1177/23998083231204689.

[4] Z. Fan, Feng, C.-C., and F. Biljecki, “Coverage and bias of street view imagery in mapping the urban environment,” Computers, Environment and Urban Systems, vol. 117, p. 102253, 2025, doi: 10.1016/j.compenvurbsys.2025.102253.

[5] J. Cinnamon, “Visual imagery and the informal city: Examining 360-degree imaging technologies for informal settlement representation,” Information Technology for Development, vol. 30, pp. 590–607, 2024, doi: 10.1080/02681102.2023.2298876.

[6] J. Zhang, Z. Yu, Y. Li, and X. Wang, “Uncovering bias in objective mapping and subjective perception of urban building functionality: A machine learning approach to urban spatial perception,” Land, vol. 12, 2023, doi: 10.3390/land12071322.

[7] F. Zhang, A. Salazar-Miranda, F. Duarte, L. J. Vale, G. Hack, M. Chen, Y. Liu, M. Batty, and C. Ratti, “Urban visual intelligence: Studying cities with artificial intelligence and street-level imagery,” Annals of the American Association of Geographers, vol. 114, pp. 876–897, 2024, doi: 10.1080/24694452.2024.2313515.

[8] S. Dai, Y. Li, A. Stein, S. Yang, and P. Jia, “Street view imagery-based built environment auditing tools: A systematic review,” International Journal of Geographical Information Science, vol. 38, pp. 1136–1157, 2024, doi: 10.1080/13658816.2024.2336034.

[9] N. Yang, Z. Deng, F. Hu, Q. Guan, Y. Chao, and L. Wan, “Urban perception assessment from street view images based on a multifeature integration encompassing human visual attention,” Annals of the American Association of Geographers, vol. 114, pp. 1424–1442, 2024, doi: 10.1080/24694452.2024.2363783.

[10] A. Foka and G. Griffin, “AI, cultural heritage, and bias: Some key queries that arise from the use of GenAI,” Heritage, vol. 7, 2024, doi: 10.3390/heritage7110287.

[11] K. Karpouzis, “Plato’s shadows in the digital cave: Controlling cultural bias in generative AI,” Electronics, vol. 13, 2024, doi: 10.3390/electronics13081457.

[12] I. Campo-Ruiz, “Artificial intelligence may affect diversity: Architecture and cultural context reflected through ChatGPT, Midjourney, and Google Maps,” Humanities and Social Sciences Communications, vol. 12, Art. no. 24, 2025, doi: 10.1057/s41599-024-03968-5.

[13] H. Aini, “Visualizing identity: Linguistic landscapes and ethnolinguistic signage in Indonesian multicultural neighborhoods,” Indonesian Journal of Language, Space, and Visual Arts, vol. 1, no. 1, pp. 51–60, 2026.

[14] D. Sartono, “Visual discourse of urban space: A semiotic analysis of street art and linguistic graffiti in Jakarta,” Indonesian Journal of Language, Space, and Visual Arts, vol. 1, no. 1, pp. 1–11, 2026.

[15] Daroji., “Architectural narratives and spatial language: A discourse study of traditional house descriptions in Indonesia,” Indonesian Journal of Language, Space, and Visual Arts, vol. 1, no. 1, pp. 12–20, 2026.

[16] P. Hidayat, “Spatial framing in tourism advertising: The linguistic construction of exoticism in Indonesian destination campaigns,” Indonesian Journal of Language, Space, and Visual Arts, vol. 1, no. 1, pp. 31–40, 2026.

[17] O. Palmini and F. Cugurullo, “Charting AI urbanism: Conceptual sources and spatial implications of urban artificial intelligence,” Discover Artificial Intelligence, vol. 3, 2023, doi: 10.1007/s44163-023-00060-w.

[18] S. E. Bibri and Z. Allam, “The metaverse as a virtual form of data-driven smart cities: The ethics of the hyper-connectivity, datafication, algorithmization, and platformization of urban society,” Computational Urban Science, vol. 2, 2022, doi: 10.1007/s43762-022-00050-1.

[19] Z. Allam, A. Sharifi, S. E. Bibri, D. S. Jones, and J. Krogstie, “The metaverse as a virtual form of smart cities: Opportunities and challenges for environmental, economic, and social sustainability in urban futures,” Smart Cities, vol. 5, no. 3, 2022, doi: 10.3390/smartcities5030040.

[20] M. Batty, “Digital twins in city planning,” Nature Computational Science, vol. 4, pp. 192–199, 2024, doi: 10.1038/s43588-024-00606-7.

[21] H. Xu, F. Omitaomu, S. Sabri, S. Zlatanova, X. Li, and Y. Song, “Leveraging generative AI for urban digital twins: A scoping review on the autonomous generation of urban data, scenarios, designs, and 3D city models for smart city advancement,” Urban Informatics, vol. 3, 2024, doi: 10.1007/s44212-024-00060-w.

[22] M. Lazzeroni and A. Romano, “Artificial intelligence and new visions of the future of the city: Exploring urban narratives through semantic and network analysis,” Journal of Urban Technology, vol. 32, pp. 63–83, 2025, doi: 10.1080/10630732.2025.2469326.

[23] Z. Cao, Y. Mao, M. Mustafa, and M. H. M. Isa, “Future cities imagined by ChatGPT-4o: Human evaluation using importance-performance analysis,” Humanities and Social Sciences Communications, vol. 12, 2025, doi: 10.1057/s41599-025-04941-6.

[24] C. Beneduce, M. Luca, and B. Lepri, “AI’s blind spots: Geographic knowledge and diversity deficit in generated urban scenario,” arXiv, 2025, doi: 10.48550/arXiv.2506.16898.

[25] S. E. Bibri, J. Huang, S. K. Jagatheesaperumal, and J. Krogstie, “The synergistic interplay of artificial intelligence and digital twin in environmentally planning sustainable smart cities: A comprehensive systematic review,” Environmental Science and Ecotechnology, vol. 20, p. 100433, 2024, doi: 10.1016/j.ese.2024.100433.