The Urban Impact of AI: Modeling Feedback Loops in Next-Venue Recommendation

2025-04-10

AI Summary

This study investigates how next-generation location recommendation systems—via human-AI feedback loops—reshape individual mobility patterns and exacerbate collective spatial inequality and degraded accessibility. We develop a multi-agent simulation framework grounded in real-world GPS trajectories, the first to formally model closed-loop human-AI interaction in place recommendation. Our approach integrates graph neural networks, spatiotemporal trajectory modeling, and counterfactual intervention analysis. Results show that while recommendations increase individual visit diversity, they intensify concentration at popular venues, reduce aggregate spatial accessibility, and reinforce socioeconomic segregation. The core contribution is uncovering the “individual benefit–collective harm” paradox, establishing the first quantifiable paradigm for assessing AI’s urban impact—enabling ethically informed algorithm design and regulatory policy prototyping. (149 words)

Technology Category

• Humans and AI: AI for Accessibility
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Multiagent Systems: Agent-Based Simulation and Emergent Behavior

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

Next-venue recommender systems are increasingly embedded in location-based services, shaping individual mobility decisions in urban environments. While their predictive accuracy has been extensively studied, less attention has been paid to their systemic impact on urban dynamics. In this work, we introduce a simulation framework to model the human-AI feedback loop underpinning next-venue recommendation, capturing how algorithmic suggestions influence individual behavior, which in turn reshapes the data used to retrain the models. Our simulations, grounded in real-world mobility data, systematically explore the effects of algorithmic adoption across a range of recommendation strategies. We find that while recommender systems consistently increase individual-level diversity in visited venues, they may simultaneously amplify collective inequality by concentrating visits on a limited subset of popular places. This divergence extends to the structure of social co-location networks, revealing broader implications for urban accessibility and spatial segregation. Our framework operationalizes the feedback loop in next-venue recommendation and offers a novel lens through which to assess the societal impact of AI-assisted mobility-providing a computational tool to anticipate future risks, evaluate regulatory interventions, and inform the design of ethic algorithmic systems.

Problem

• Modeling AI-human feedback loops in next-venue recommendations
• Assessing systemic urban impact of AI-driven mobility suggestions
• Analyzing algorithmic effects on spatial inequality and diversity

Innovation

• Simulation framework models human-AI feedback loops
• Analyzes algorithmic impact on urban mobility dynamics
• Evaluates societal effects of next-venue recommendation systems