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Soc."],"published-print":{"date-parts":[[2025,6,30]]},"abstract":"<jats:p>Urban population growth has significantly complicated the management of mobility systems, demanding innovative tools for planning. Generative Crowd-Flow\u00a0 (GCF) models, which leverage machine learning to simulate urban movement patterns, offer a promising solution but lack sufficient evaluation of their fairness\u2013a critical factor for equitable urban planning. We present an approach to measure and benchmark the fairness of GCF\u00a0 models by developing a first-of-its-kind set of fairness metrics specifically tailored for this purpose. Using observed flow data, we employ a stochastic biased sampling approach to generate multiple permutations of Origin-Destination\u00a0 datasets, each demonstrating intentional bias. Our proposed framework allows for the comparison of multiple GCF\u00a0 models to evaluate how models introduce bias in outputs. Preliminary results indicate a tradeoff between model accuracy and fairness, underscoring the need for careful consideration in the deployment of these technologies. 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