Detecting disparities in police deployments using dashcam data
Detecting disparities in police deployments using dashcam data
复制标题
DOI：
10.1145/3593013.3594020
复制
发表时间：
2023-05
期刊：
影响因子：
--
通讯作者：
Matt W Franchi;J.D. Zamfirescu-Pereira;Wendy Ju;E. Pierson
中科院分区：
文献类型：
--
作者：
Matt W Franchi;J.D. Zamfirescu-Pereira;Wendy Ju;E. Pierson
大规模警务数据对于检测警察行为和警务算法中的不公平至关重要。然而，一种重要的警务数据在美国仍然基本上无法获得：汇总的警察部署数据，捕捉哪些社区有最重的警察存在。在这里，我们表明，警察部署水平的差异可以量化检测警车在dashcam图像的公共街道场景。使用来自纽约市共乘司机的24，803，854张仪表盘图像的数据集，我们发现可以高精度地检测到警车（平均精度为0.82，AUC为0.99），并识别出233，596张包含警车的图像。各社区的警车部署水平存在很大的不平等。部署水平最高的社区比部署水平最低的社区高出近20倍。两种截然不同的地区经历了高警车部署- 1）密集，高收入，商业区和2）黑人和西班牙裔居民比例较高的低收入社区。我们讨论了这些差异对警务公平和警务数据训练算法的影响。
Large-scale policing data is vital for detecting inequity in police behavior and policing algorithms. However, one important type of policing data remains largely unavailable within the United States: aggregated police deployment data capturing which neighborhoods have the heaviest police presences. Here we show that disparities in police deployment levels can be quantified by detecting police vehicles in dashcam images of public street scenes. Using a dataset of 24,803,854 dashcam images from rideshare drivers in New York City, we find that police vehicles can be detected with high accuracy (average precision 0.82, AUC 0.99) and identify 233,596 images which contain police vehicles. There is substantial inequality across neighborhoods in police vehicle deployment levels. The neighborhood with the highest deployment levels has almost 20 times higher levels than the neighborhood with the lowest. Two strikingly different types of areas experience high police vehicle deployments — 1) dense, higher-income, commercial areas and 2) lower-income neighborhoods with higher proportions of Black and Hispanic residents. We discuss the implications of these disparities for policing equity and for algorithms trained on policing data.