《Last Week Tonight》节目主持人约翰·奥利弗对美国警察部门使用的各类监控技术进行了严厉批评。他指出，摄像头、车牌识别仪、麦克风和手机信号模拟器等工具的广泛应用存在重大隐私和民权风险，容易被滥用且常导致误判，在有色人种社区尤为过度使用。[1]
奥利弗通过具体数据揭示了监控范围之广。新奥尔良部署了超过2000个监控摄像头，而美国全国有85000个摄像头可供警方请求使用。[1]超过200个美国司法管辖区采用了ShotSpotter微音系统用于检测枪声。[1]自动车牌识别器的误报率同样令人担忧，至少35%的警报被证实为误报。[1]洛杉矶、芝加哥和费城等主要城市警察部门已开始使用预测性警务技术。[1]
奥利弗强调了这些技术的潜在危害。他指出这类监控工具"可以追踪你去哪里，这意味着他们可能发现，比如说，你是否参加了抗议活动或访问了堕胎诊所"。[1]一名警察曾表示，没有摄像头"绝对"无法确定最近案件中谁是射手、谁是受害者，体现了警方对这些技术的依赖程度。[1]
Comedian John Oliver has launched a scathing critique of surveillance technologies deployed by U.S. police departments on his show Last Week Tonight, describing the systems as "Big Brother on steroids."[1] The comedian highlighted an array of monitoring tools now commonplace across American law enforcement, including surveillance cameras, license plate readers, acoustic sensors, and cell phone signal simulators, arguing these technologies pose serious threats to privacy and civil liberties.[1]
The scope of police surveillance infrastructure is staggering. New Orleans alone operates over 2,000 surveillance cameras, while approximately 85,000 cameras across the United States are available for police to access upon request.[1] Beyond visual surveillance, more than 200 American jurisdictions have adopted ShotSpotter acoustic monitoring systems to detect gunshots.[1] Additionally, major police departments in Los Angeles, Chicago, and Philadelphia now employ predictive policing technology.[1]
Oliver emphasized the risks of algorithmic error and potential for abuse. Automatic license plate readers generate false alerts at least 35 percent of the time, according to data he presented.[1] One police officer acknowledged that without camera footage, determining who fired a weapon versus who was victimized in recent cases would be "absolutely" impossible.[1] The comedian warned that surveillance capabilities enable authorities to track individuals' movements and potentially discover sensitive information, such as whether someone attended a protest or visited an abortion clinic.[1] Oliver argued these technologies are disproportionately deployed in communities of color, where misidentification and misuse present heightened risks.[1]