Image Generative AI to Design Public Spaces: a Reflection of how AI Could Improve Co-Design of Public Parks

Abstract

Image generative AI (IGAI) could change how policymakers engage with the public to design public spaces, facilitating how designers translate the public’s desires into features. However, using IGAI has challenges, such as encoded biases, which might reinforce stereotypes and harm underrepresented communities. We conducted a case study to explore how using IGAI alters the co-design process of public parks through public engagement. We use data collected from interviews with immigrants discussing the Puente Hills Landfill Park design in Los Angeles, which will re-purpose a former landfill into a new public park. We use Dream Studio as a Design Probe, generating images from the interviewees’ insights and critically reflecting on the design process through internal interviews and a reflective workshop. We analyze our case in three domains: Opportunities, Risks and Challenges, and Features and Requirements. In the opportunities domain, we discuss how the enhanced translation of words to images changes the relationship between stakeholder engagement, multiplicity, and efficiency. In the risks and challenges domain, we discuss how IGAI might enhance power imbalances and biases. Finally, we reflect on what features would ease the safe and responsible use of IGAI to engage citizens in co-designing public parks.