Predictive policing systems that direct patrol resources based on algorithmically generated crime forecasts have been widely deployed across US cities, yet their tendency to encode and amplify racial disparities remains poorly understood in quantitative terms. We present a reproducible simulation framework that couples a Generative Adversarial Network GAN with a Noisy OR patrol detection model to measure how racial bias propagates through the full enforcement pipeline from crime occurrence to police contact. Using 145000 plus Part 1 crime records from Baltimore 2017 to 2019 and 233000 plus records from Chicago 2022, augmented with US Census ACS demographic data, we compute four monthly bias metrics across 264 city year mode observations: the Disparate Impact Ratio DIR, Demographic Parity Gap, Gini Coefficient, and a composite Bias Amplification Score. Our experiments reveal extreme and year variant bias in Baltimores detected mode, with mean annual DIR up to 15714 in 2019, moderate under detection of Black residents in Chicago DIR equals 0.22, and persistent Gini coefficients of 0.43 to 0.62 across all conditions. We further demonstrate that a Conditional Tabular GAN CTGAN debiasing approach partially redistributes detection rates but cannot eliminate structural disparity without accompanying policy intervention. Socioeconomic regression analysis confirms strong correlations between neighborhood racial composition and detection likelihood Pearson r equals 0.83 for percent White and r equals negative 0.81 for percent Black. A sensitivity analysis over patrol radius, officer count, and citizen reporting probability reveals that outcomes are most sensitive to officer deployment levels. The code and data are publicly available at this repository.

翻译：预测性警务系统通过算法生成的犯罪预测来指导巡逻资源分配，已在美国各城市广泛部署，但其编码和放大种族差异的倾向在定量层面仍缺乏深入理解。我们提出一个可复现的模拟框架，该框架将生成对抗网络（GAN）与噪声OR巡逻检测模型相结合，用以衡量种族偏见如何从犯罪发生到警方接触的完整执法链中传播。利用2017至2019年巴尔的摩超过14.5万条一级犯罪记录、2022年芝加哥超过23.3万条记录，并结合美国人口普查局ACS人口统计数据，我们计算了264个城市年-月观测值上的四项月度偏见指标：差异性影响比（DIR）、人口统计平等差距、基尼系数以及复合偏见放大分数。实验揭示了巴尔的摩检测模式中极端且逐年变化的偏见：2019年年度平均DIR高达15714，芝加哥对非裔居民的检测不足程度适中（DIR=0.22），所有条件下的基尼系数持续在0.43至0.62之间。我们进一步证明，条件式表格GAN（CTGAN）去偏见方法可部分重新分配检测率，但若无配套政策干预，无法消除结构性差异。社会经济回归分析证实，社区种族构成与检测概率之间存在强相关性（白人比例Pearson r=0.83，非裔比例Pearson r=-0.81）。针对巡逻半径、警员数量和公民举报概率的敏感性分析表明，结果对警员部署水平最为敏感。代码和数据已公开于相应代码库。