While leveraging urban streetscape data and deep learning is highly effective for identifying general patterns and analyzing large areas in urban public space studies, its utility is somewhat limited when seeking specific design recommendations. This approach tends to overlook smaller yet impactful elements, such as wires and seating, due to their minimal visual area in streetscape data. This limitation constrains the depth of insights into how all elements influence users' spatial perceptions in urban public spaces, particularly in identifying unrepresented elements or gathering users' needs for spatial interventions. In response to this challenge, our study focused on Beijing's public spaces to discern which urban elements impact users' spatial feelings and how adjustments to these elements could foster more positive urban public environments. Through field and online surveys across 32 public spaces, involving feedback from approximately 2000 respondents, and employing computer vision and natural language processing for data analysis, we established a pathway from user perceptions to actionable urban design interventions. This method aims to offer an "end-to-end" strategy for improving urban public spaces by transitioning from user experiences to the implementation of designs that cater to user needs. Although our initial findings call for further statistical scrutiny and broader empirical validation, our objective is to develop a more integrated approach to urban survey and analysis that can directly inform urban planners and designers, reducing reliance on extensive datasets and advanced technical tools.

Humans can only see a small portion of the electromagnetic spectrum ranging from red to violet. Yet, the world around us is awash with invisible electromagnetic waves, including radio frequency (RF) waves that are used for telecommunication. These RF waves are emitted from cell towers, mobile phones, routers, and any device that can “connect” with others. Researchers are currently refining how to “see” using RF wave receivers. RF “vision” can “see” through walls and in the dark without any cameras, allowing for ubiquitous, unobtrusive surveillance. This talk compares this upcoming technology with cameras, questioning “what’s the deal, anyway, with humans and surveillance?” and how do we determine ethical use.

In the complex tapestry of urban governance, the traditionally perceived trade-off between accountability and discretion is undergoing a seismic shift with the advent of Artificial Intelligence (AI) in recent years. This article investigates the transformative role of AI in bureaucratic systems, offering blueprints for future-ready urban governance. At the center of this study is a further exploration of "accountable discretion" (Goldsmith & Crawford, 2014), which captures the evolving dynamics between technologies and bureaucracy, suggesting a pathway for governmental agencies to recalibrate the delicate balance between discretion and accountability. The working paper brings forward two important propositions. First, cities, with their inherent complexities, cannot solely rely on technological determinism for progress. They demand a symbiotic interplay between human intuition, judgment, and technological capabilities. Here, AI emerges not as a substitute but as a tool, amplifying and refining human decision-making. By drawing on AI's computational prowess, bureaucrats are equipped with data-driven insights, facilitating nuanced, adaptable, and efficient decision-making processes. Concurrently, AI's real-time monitoring, analytics, and predictive functionalities ensure that this broadened discretion is exercised within the bounds of ethical, legal, and societal norms. Second, the paper discusses how AI, when astutely integrated into bureaucratic systems, can both expand the discretionary space for bureaucrats and simultaneously maintain rigorous standards of accountability. This transformative shift may redefine the traditional trade-offs between bureaucrats’ discretion (Lipsky, 1980)) and accountability in urban governance (Bovens, 2007; 2010), championing an equilibrium that leverages technological advancements without sidelining core governance values. The paper further delves into real-world applications, from tax filing evaluations to monitoring outsourced services, presenting AI as a linchpin in modernizing governance. However, the road ahead is not without its crevices: issues of transparency, cybersecurity, and privacy demand attention. Our exploration underscores the importance of governance-promoting technologies, particularly in the course of AI-enhanced governance. Cities are championed as active agents in shaping human society, not merely as passive recipients of technological innovations. This intertwined relationship between humans and AI offers a pragmatic pathway for cities of the future to navigate the challenges, aspiring for governance strategies that are transparent, effective, and adaptive to the ever-evolving urban milieu.

Since its inception in the late 1950s, Artificial Intelligence has been extensively researched, giving rise to many approaches within the field. Notably, the recent surge in demand for these new technologies has underscored the imperative of adapting them across diverse contexts, including architecture (Chaillou, 2021). However, as AI’s impact on architecture remains in its early stages, a comprehensive delineation of its benefits and drawbacks is yet to be achieved, particularly in a specialised field characterised by abundant data resources and emerging applications. Can Artificial Intelligence and Machine Learning, utilising the extensive urban architectural datasets, derive meaningful residential typologies from their morphological indicators? This research paper introduces a data-driven methodology designed to yield valuable insights for architects, urban designers, and planners. It emphasises the pivotal role of morphological analysis in comprehending the spatial characteristics and composition of residential buildings within urban landscapes. This approach leverages building footprint data and its inherent embedded information, focusing on a specific case study, the city of Amsterdam. The paper encompasses four distinct research domains: an exhaustive examination of accessible footprint data, advocating for high-quality and easily accessible data sources; the exploration of morphological geometry encoding, delving into shape definition methodologies; typology identification, where Machine Learning clustering techniques are investigated and deployed for optimal outcomes; and finally, the proposition of an analytical tool catering to remote analysts and individuals necessitating comprehensive urban and architectural information. Central to this work are the derived typologies, a systematic framework enabling the assessment of the heterogeneous building forms, sizes, and distributions that collectively shape our urban environments. These typologies yield promising outcomes, indicating that this approach could effortlessly bridge analyses across diverse domains of expertise and knowledge. This research illuminates the expanding potential of AI and Machine Learning in the field of architecture. The analytical framework presented here carries wide-reaching implications, envisioning a tool to develop our perception of the cities and buildings that make up our urban environments. Keywords: Data-driven; Morphology; Residential Buildings; Typologies; Analytical Tool.