Algorithmic Bias and Defensive Placemaking: Implications of Generative AI Co-Creation for Urban Digital Twins

Abstract

Urban Digital Twins excel at modeling physical infrastructure but remain structurally limited in capturing the qualitative, experiential dimensions of urban life—particularly sense of place, which empirical research links to civic stewardship and long-term sustainability. This study investigates whether generative AI can serve as a participatory elicitation interface for surfacing these missing human data layers. Through a mixed-methods experimental design, 24 residents of Austin, Texas, each selected a personally meaningful public urban space and created visual representations using both hand-drawn sketching and iterative co-creation with the text-to-image model DALL-E. Pre- and post-experiment surveys and semi-structured interviews captured participants’ perceptions of the outputs and self-reported shifts in place awareness. The findings reveal a dialectical tension: DALL-E consistently defaulted to generic visual archetypes, overriding participants’ localized descriptions. However, this algorithmic homogenization paradoxically deepened participants’ sense of place through a process we term ‘validation by contrast’—residents utilized the AI’s inaccurate outputs as a foil to consciously articulate what made their environments authentically meaningful. These findings suggest that for human-centric Digital Twins, the actionable data lies not in the AI-generated image itself, but in the negotiation process through which residents defend and crystallize their authentic spatial identity. Full empirical validation of this pattern, including systematic comparison across representation modalities, is reserved for future work.