arXiv AI
Multimodal Large Language Models Predict Urban Safety Perception but Encode Non-Neutral Demographic Priors
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv Computation and Language
4d ago
Robots in the City: Persona-Based Perceptions of Urban Robots
arXiv:2611.05954v1 Announce Type: cross Abstract: Pedestrians increasingly encounter robots in urban public spaces without interacting with them, but how robots change the perceived safety of a stree...
arXiv Computation and Language
Sep 25
Persona Prompting in Multimodal Urban Perception: Descriptive Convergence and Interpretive Variation
The paper investigates how persona prompting influences the language produced by two multimodal large language models—Qwen3‑VL and Gemma4—when describing urban scenes. Outputs are categorized into descriptive grounding (captions), perception tags, and interpretive framing (justifications). Results show captions largely converge across persona profiles, while justifications differ markedly, especially along economic status, political orientation, and personality dimensions, with economic status producing the greatest variation. Perception tags also reflect attribute similarities, and exploratory topic analysis indicates persona‑specific evaluative emphasis. Overall, persona prompting has a stronger effect on interpretive framing than on descriptive grounding.
arXiv AI
Aug 28
Is Your Neighborhood Safe? Place-based Stigma in Large Language Models' Urban Safety Judgments
Large language models (LLMs) are increasingly used to guide urban safety decisions, but this study shows that their judgments are more influenced by neighborhood names than by geographic coordinates. Across seven instruct‑tuned models tested on 186 neighborhoods in Los Angeles and Chicago, name‑based ratings varied significantly and correlated with the proportion of locally dominant marginalized groups, while coordinate‑only ratings remained largely flat. The research finds that removing neighborhood names reduces both bias and accuracy, highlighting the complex role of demographic stereotypes and crime signals in LLM safety assessments.
arXiv AI
Jun 16
UrbanWell: Benchmarking Multimodal Large Language Models for Spatio-Temporal Urban Wellbeing Analytics
arXiv:2606. 15890v1 Announce Type: new Abstract: Understanding urban wellbeing from multimodal data requires integrating heterogeneous spatial and temporal signals, posing significant challenges for current multimodal large language models (MLLMs).
arXiv Computer Vision
Oct 16
AI Knows Where You Are: Exposure, Bias, and Inference in Multimodal Geolocation with KoreaGEO
The paper introduces KoreaGEO, a fine‑grained, multimodal, privacy‑aware benchmark for image‑based geolocation built from Korean street views. It covers four socio‑spatial clusters, nine place types, and includes two captioning styles that mimic real‑world privacy exposure. Using a three‑path protocol (image‑only, functional‑caption, high‑risk‑caption), the study shows that input modality has a stronger effect on localization accuracy and privacy risk than model size or architecture, and reveals structural prediction biases toward core cities.
arXiv Computer Vision
Sep 25
A Vision-Language Framework for Measuring Social Life on Sidewalks
The paper introduces a vision‑language framework that extracts social indicators from street‑level imagery, converting panoramic views into sidewalk‑facing sideviews with timestamps. Using a VLM‑based activity detection system, it codes each pedestrian across ten observable dimensions, producing a Social Dwelling Index (SDI) that captures grouping, dwelling, activity diversity, and accessibility flags. Applied to over 100,000 sideviews in New York City, the study finds that pedestrian volume and SDI are only weakly correlated, indicating that high foot traffic does not necessarily equate to intense social activity.