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The concept of 'digital twins' has gained significant traction in urban planning, referring to virtual replicas of physical cities that simulate real-time data from sensors, infrastructure, and citizen behavior. Proponents argue that these dynamic models enable predictive governance, allowing authorities to test scenarios such as flood responses or traffic congestion before implementing costly physical changes. However, critics caution that over-reliance on digital twins may exacerbate existing inequalities, as data collection often prioritizes affluent neighborhoods with better sensor coverage, leading to skewed resource allocation. Furthermore, the opacity of algorithmic decision-making raises accountability concerns, particularly when automated systems recommend policies that affect vulnerable populations without transparent justification. While digital twins offer promising tools for efficiency, their deployment must be accompanied by rigorous ethical frameworks and inclusive data practices to avoid reinforcing systemic biases.