Big Data and Predictive Reasonable Suspicion
Big Data and Predictive Reasonable Suspicion
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大数据与预测合理怀疑
DOI：
10.2139/ssrn.2394683
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
A. Ferguson
中科院分区：
文献类型：
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作者：
A. Ferguson
第四修正案要求“合理怀疑”逮捕嫌疑人。一般来说，怀疑来自警官观察或了解的信息。它是针对特定地点的特定人的。大多数合理的怀疑案件涉及警察面对从事可观察到的可疑活动的未知嫌疑人。从本质上讲，合理怀疑原则是基于“小数据”-涉及有限信息和对嫌疑人知之甚少的离散事实。但是，如果这些小数据被“大数据”所取代呢？如果警察可以通过新的网络信息来源“了解”嫌疑人呢？或者，如果预测分析可以预测谁可能是社区中的麻烦制造者呢？大数据技术的兴起对第四修正案的传统范式提出了挑战。现在，不费吹灰之力，大多数未知的嫌疑人就可以被“知道”，因为信息网络可以独立于警官的观察来识别和提供关于嫌疑人的广泛的个人数据。新的数据源，包括执法数据库、第三方信息源（电话记录、租赁记录、GPS数据、视频监控数据等），而预测分析与生物识别或面部识别软件相结合，意味着可以在几次数据搜索中了解有关嫌疑人的信息。在某个时候，数据（独立于观察）可能变得足够个性化和预测性，以证明扣押嫌疑人是合理的。这篇文章提出的问题是，第四修正案的停止是否可以基于特定的，个性化的，但在其他方面非犯罪因素的集合？本文追溯了从“小数据”合理怀疑理论（侧重于未知嫌疑人的具体、可观察的行动）到“大数据”现实（已知嫌疑人的互联信息丰富的世界）转变的后果。有了更有针对性的信息，街上的警察将对他们观察到犯罪活动的可能性有更强的预测能力。然而，这种演变只是暗示了大数据监管的前景。下一阶段将使用现有的预测分析来锁定嫌疑人，而无需对犯罪活动进行任何实际观察，仅仅依靠各种数据点的积累。未知的嫌疑人将成为已知的，不是因为他们是谁，而是因为他们留下的数据。通过网络数据库使用模式匹配技术，个人将从大量的信息数据流中脱颖而出。这一新的现实颠覆了合理怀疑，使之从防止不合理拦截的一种保护手段，转变为证明这些拦截是正当的一种手段。
The Fourth Amendment requires “reasonable suspicion” to seize a suspect. As a general matter, the suspicion derives from information a police officer observes or knows. It is individualized to a particular person at a particular place. Most reasonable suspicion cases involve police confronting unknown suspects engaged in observable suspicious activities. Essentially, the reasonable suspicion doctrine is based on “small data” – discrete facts involving limited information and little knowledge about the suspect. But what if this small data is replaced by “big data”? What if police can “know” about the suspect through new networked information sources? Or, what if predictive analytics can forecast who will be the likely troublemakers in a community? The rise of big data technology offers a challenge to the traditional paradigm of Fourth Amendment law. Now, with little effort, most unknown suspects can be “known,” as a web of information can identify and provide extensive personal data about a suspect independent of the officer’s observations. New data sources including law enforcement databases, third party information sources (phone records, rental records, GPS data, video surveillance data, etc.), and predictive analytics, combined with biometric or facial recognition software, means that information about that suspect can be known in a few data searches. At some point, the data (independent of the observation) may become sufficiently individualized and predictive to justify the seizure of a suspect. The question this article poses is can a Fourth Amendment stop be predicated on the aggregation of specific, individualized, but otherwise non-criminal factors?This article traces the consequences in the shift from a “small data” reasonable suspicion doctrine, focused on specific, observable actions of unknown suspects, to the “big data” reality of an interconnected information rich world of known suspects. With more targeted information, police officers on the streets will have a stronger predictive sense about the likelihood that they are observing criminal activity. This evolution, however, only hints at the promise of big data policing. The next phase will be using existing predictive analytics to target suspects without any actual observation of criminal activity, merely relying on the accumulation of various data points. Unknown suspects will become known, not because of who they are but because of the data they left behind. Using pattern matching techniques through networked databases, individuals will be targeted out of the vast flow of informational data. This new reality subverts reasonable suspicion from being a source of protection against unreasonable stops, to a means of justifying those same stops.