We analyze changes in pedestrian behavior over a 30-y period in four urban public spaces located in New York, Boston, and Philadelphia. Building on William Whyte’s observational work, which involved manual video analysis of pedestrian behaviors, we employ computer vision and deep learning techniques to examine video footage from 1979–80 and 2008–10. Our analysis measures changes in walking speed, lingering behavior, group sizes, and group formation. We find that the average walking speed has increased by 15%, while the time spent lingering in these spaces has halved across all locations. Although the percentage of pedestrians walking alone remained relatively stable (from 67% to 68%), the frequency of group encounters declined, indicating fewer interactions in public spaces. This shift suggests that urban residents are using streets as thoroughfares rather than as social spaces, which has important implications for the role of public spaces in fostering social engagement.
Computational Public Space
Human social sensing is an untapped resource for computational social science
The ability to ‘sense’ the social environment and thereby to understand the thoughts and actions of others allows humans to fit into their social worlds, communicate and cooperate, and learn from others’ experiences. Here we argue that, through the lens of computational social science, this ability can be used to advance research into human sociality. When strategically selected to represent a specific population of interest, human social sensors can help to describe and predict societal trends. In addition, their reports of how they experience their social worlds can help to build models of social dynamics that are constrained by the empirical reality of human social systems.
The Local Connection Crisis: New Data on What Communities Need
Our Civic Signals research | New_ Public
The Civic Signals are 14 indicators of healthy online spaces, based on years of research into what makes online communities work.
Trails and public spaces — Rafael M. Batista
A reply to Kenny Peng and colleagues on designing social media around trails — and why the campground or the piazza may be the better analogy.
Ordinary Abundance
A walk through a modern apartment, through the eyes of the people for whom everything in it was new.
Ordinary Abundance
A walk through a modern apartment, through the eyes of the people for whom everything in it was new.
AI Behavioral Science
We outline a foundation for a new field of ``AI Behavioral Science,'' covering three perspectives. First, as AI becomes ubiquitous and is increasingly proprietary and opaque, it becomes vital to develop techniques for assessing AI behavior. We outline how tools developed to assess people's behaviors by social scientists can be used to assess and infer AI's behaviors biases, tendencies, and heuristics. Second, we also discuss how AI can change the ways in which we learn about human behavior. Beyond its computational power, AI offers new techniques for simulating, inferring, and predicting human behaviors that we outline and discuss. Third, as humans and AI are interacting in increasingly complex and intertwined systems, we need to understand the implications for the resulting economic and political outcomes. We outline issues that are increasingly pressing concerning the future of human-AI interactions and potential changes and disruptions that can ensue.
AI Behavioral Science: A Framework and Agenda
We discuss the challenges and opportunities present in the rapidly emerging area of “AI Behavioral Science.” We frame it via three subfields. First, as AI becomes ubiquitous and is increasingly proprietary and opaque, it becomes vital to develop models of AI and methods for assessing AI behavior. We outline how tools developed to assess people’s behaviors by social scientists can be used to model, assess and infer AI’s behaviors biases, tendencies, and heuristics. Second, we also discuss how AI can change the ways in which we learn about human behavior. Beyond its computational power, AI offers new techniques for simulating, inferring, predicting, and analyzing human behaviors. Third, as humans and AI are interacting in increasingly complex and intertwined systems, we need to analyze and model human-AI interactions including how human and AI behaviors depend on interactions at the individual level, how interacting systems of humans and AI behave, and ultimately how AI’s integration into society affects economic and political outcomes. We discuss current research, questions, agendas, and goals in each of these three subfields and how they depend upon each other.
The curb-cut effect
The curb-cut effect illustrates how when we design to benefit disadvantaged or vulnerable groups we end up helping society as a whole. Angela Glover Blackwell explains how campaigning by students with disabilities in Berkeley in the early 1970s led to adding curb cuts to the Berkeley sidewalks to make access easier for those in wheelchairs. Yet it wasn't just people in wheelchairs that it helped. Curb cuts also made life easier for people pushing children in strollers, people using trolleys for deliveries, people pulling a suitcase, those wheeling bikes or on skateboards, and it also helps save lives by guiding people to cross at safe locations. Another example is adding closed captioning to TV that helps anyone watch in a noisy bar, a waiting room, or watching an airline safety video. Or a classic example of universal design in the OXO Good Grips range originally made to be comfortable for holding a peeler even if you have arthritis. It's also a useful analogy for "how laws and programs designed to benefit vulnerable groups, such as the disabled or people of color, often end up benefiting all," (PolicyLink) whether that be increasing broadband access, improving public transport or taking cuts out of curbs. Here's a short, fun video of Gary Karp explaining the curb-cut effect. Thanks to my patron Quintin Balsdon for sharing it with me.
Building Roundabout: A Pro-Social Platform for Local Communities
A conversation with the team behind New_ Public's Roundabout.
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Deepfake detection by human crowds, machines, and machine-informed crowds
Significance The recent emergence of deepfake videos raises theoretical and practical questions. Are humans or the leading machine learning model more capable of detecting algorithmic visual manipulations of videos? How should content moderation systems be designed to detect and flag video-based misinformation? We present data showing that ordinary humans perform in the range of the leading machine learning model on a large set of minimal context videos. While we find that a system integrating human and model predictions is more accurate than either humans or the model alone, we show inaccurate model predictions often lead humans to incorrectly update their responses. Finally, we demonstrate that specialized face processing and the ability to consider context may specially equip humans for deepfake detection. , The recent emergence of machine-manipulated media raises an important societal question: How can we know whether a video that we watch is real or fake? In two online studies with 15,016 participants, we present authentic videos and deepfakes and ask participants to identify which is which. We compare the performance of ordinary human observers with the leading computer vision deepfake detection model and find them similarly accurate, while making different kinds of mistakes. Together, participants with access to the model’s prediction are more accurate than either alone, but inaccurate model predictions often decrease participants’ accuracy. To probe the relative strengths and weaknesses of humans and machines as detectors of deepfakes, we examine human and machine performance across video-level features, and we evaluate the impact of preregistered randomized interventions on deepfake detection. We find that manipulations designed to disrupt visual processing of faces hinder human participants’ performance while mostly not affecting the model’s performance, suggesting a role for specialized cognitive capacities in explaining human deepfake detection performance.
Deepfake detection by human crowds, machines, and machine-informed crowds
Significance The recent emergence of deepfake videos raises theoretical and practical questions. Are humans or the leading machine learning model more capable of detecting algorithmic visual manipulations of videos? How should content moderation systems be designed to detect and flag video-based misinformation? We present data showing that ordinary humans perform in the range of the leading machine learning model on a large set of minimal context videos. While we find that a system integrating human and model predictions is more accurate than either humans or the model alone, we show inaccurate model predictions often lead humans to incorrectly update their responses. Finally, we demonstrate that specialized face processing and the ability to consider context may specially equip humans for deepfake detection. , The recent emergence of machine-manipulated media raises an important societal question: How can we know whether a video that we watch is real or fake? In two online studies with 15,016 participants, we present authentic videos and deepfakes and ask participants to identify which is which. We compare the performance of ordinary human observers with the leading computer vision deepfake detection model and find them similarly accurate, while making different kinds of mistakes. Together, participants with access to the model’s prediction are more accurate than either alone, but inaccurate model predictions often decrease participants’ accuracy. To probe the relative strengths and weaknesses of humans and machines as detectors of deepfakes, we examine human and machine performance across video-level features, and we evaluate the impact of preregistered randomized interventions on deepfake detection. We find that manipulations designed to disrupt visual processing of faces hinder human participants’ performance while mostly not affecting the model’s performance, suggesting a role for specialized cognitive capacities in explaining human deepfake detection performance.
Experienced Segregation
We estimate a measure of segregation, experienced isolation, that captures individuals’ exposure to diverse others in the places they visit over the course of their days. Using Global Positioning System (GPS) data collected from smartphones, we measure experienced isolation by race. We find that the isolation individuals experience is substantially lower than standard residential isolation measures would suggest, but that experienced and residential isolation are highly correlated across cities. Experienced isolation is lower relative to residential isolation in denser, wealthier, more educated cities with high levels of public transit use, and is also negatively correlated with income mobility.
Terminal: Live Activity in AI Agent Spaces | Concept Country
A live terminal of 72 public AI agent social spaces, sampled every 15 minutes and the busiest every minute: posts per hour, active authors, outages, and a heat map of spam versus diverse conversation.
Photos: Hundreds protest at Open AI, Anthropic offices in San Francisco
Protesters met in front of OpenAI's headquarters in Mission Bay before marching to the offices of Anthropic and Google DeepMind.
