Advancing Transparency and Understandability of Urban AI for Citizens Through Interactive Public Displays
Komal, Maria (2025)
2025
Master's Programme in Computing Sciences and Electrical Engineering
Informaatioteknologian ja viestinnän tiedekunta - Faculty of Information Technology and Communication Sciences
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Hyväksymispäivämäärä
2025-05-28
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:tuni-202505286293
https://urn.fi/URN:NBN:fi:tuni-202505286293
Tiivistelmä
Artificial Intelligence (AI) systems are increasingly embedded in urban environments, yet their operations often remain opaque to citizens which limits public understanding and raises concerns. This research explores how urban AI systems can be made more transparent and understandable to residents through a Research-through-Design (RtD) approach. The study addresses three re-search questions: (1) How do citizens perceive and interact with urban AI in their daily lives, (2) What information needs do citizens have regarding AI in urban contexts, (3) What kind of design of an interactive public display can advance understanding and perceived transparency of AI in urban environments. To investigate these questions, an interactive Figma prototype simulating three urban AI use cases (e.g., MoodFit activities, urban assistant, and air quality monitor) was developed. The prototype presents AI system information through layered, user-controlled expla-nations. Through the prototype design, the research identifies information that can help improve the understanding of urban AI and how situated interactions can enhance AI literacy by providing clear, step-by-step explanations of AI.
Prototype evaluations were conducted with 10 participants aged 20–50 in Tampere’s public and semi-public spaces, selected via convenience sampling method. Evaluations were followed by semi structured interviews to gather participants perspectives. Demographic data was collected through pre-evaluation surveys. This mix method approach provided qualitative data which is analysed through the thematic analysis method to gather study insights. Findings indicate that simplified, non-technical explanations of AI’s functionality, benefits, risks and capabilities are more effective for comprehension rather than complex technical algorithmic details. Participants expressed particular interest in understanding AI’s data handling practices which highlights the need for thoughtful information design. The study proposes Interactive Public Displays (IPDs) as an effective medium for disseminating AI knowledge, with installation preferences for high-traffic locations like train stations and shopping malls. By embedding interactive learning experiences in urban contexts, this approach promotes active engagement, situated learning and supports informed participation in AI-driven environments. The study contributes (1) identification of es-sential AI-related information for citizens, (2) empirical validation of a UI design to make urban AI concepts familiar to public, and (3) proposal of IPDs as a tangible medium for public AI literacy.
The study’s limitations include a partially functional prototype and small sample size, however, the exploratory nature of the study provided valuable initial findings. The study offers foundational insights for future work on human-AI interaction, ethical urban AI, socially transparent urban AI, and AI literacy. Future research should focus on developing and deploying functional prototypes in real-world settings, adapting explanations to diverse user needs, and evaluating with IPDs for authentic user feedback. This work advances socially transparent, human-centered urban AI by demonstrating how UI design can empower citizens, build trust, and enhance transparency and understandability of AI systems in smart cities.
Prototype evaluations were conducted with 10 participants aged 20–50 in Tampere’s public and semi-public spaces, selected via convenience sampling method. Evaluations were followed by semi structured interviews to gather participants perspectives. Demographic data was collected through pre-evaluation surveys. This mix method approach provided qualitative data which is analysed through the thematic analysis method to gather study insights. Findings indicate that simplified, non-technical explanations of AI’s functionality, benefits, risks and capabilities are more effective for comprehension rather than complex technical algorithmic details. Participants expressed particular interest in understanding AI’s data handling practices which highlights the need for thoughtful information design. The study proposes Interactive Public Displays (IPDs) as an effective medium for disseminating AI knowledge, with installation preferences for high-traffic locations like train stations and shopping malls. By embedding interactive learning experiences in urban contexts, this approach promotes active engagement, situated learning and supports informed participation in AI-driven environments. The study contributes (1) identification of es-sential AI-related information for citizens, (2) empirical validation of a UI design to make urban AI concepts familiar to public, and (3) proposal of IPDs as a tangible medium for public AI literacy.
The study’s limitations include a partially functional prototype and small sample size, however, the exploratory nature of the study provided valuable initial findings. The study offers foundational insights for future work on human-AI interaction, ethical urban AI, socially transparent urban AI, and AI literacy. Future research should focus on developing and deploying functional prototypes in real-world settings, adapting explanations to diverse user needs, and evaluating with IPDs for authentic user feedback. This work advances socially transparent, human-centered urban AI by demonstrating how UI design can empower citizens, build trust, and enhance transparency and understandability of AI systems in smart cities.