Technologies of Crime Prediction: The Reception of Algorithms in Policing and Criminal Courts
Technologies of Crime Prediction: The Reception of Algorithms in Policing and Criminal Courts
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DOI：
10.1093/socpro/spaa004
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发表时间：
2021-08-01
期刊：
影响因子：
3.2
通讯作者：
Christin, Angele
中科院分区：
文献类型：
--
作者：
Brayne, Sarah;Christin, Angele
美国刑事司法系统中使用的预测技术的数量正在增加。然而，迄今为止，关于刑事司法机构对算法的接受情况的研究还很少。我们利用在大型城市警察局和中型刑事法院进行的人种学实地调查来评估预测技术在刑事司法过程不同阶段的影响。我们首先表明，类似的论点被用来证明执法和刑事法庭采用预测算法的合理性。在这两种情况下，算法都被描述为比人类的自由判断更加客观和高效。然后，我们研究如何使用预测算法，记录执法和法律专业人员之间类似的职业抵抗过程。在这两种情况下，对去技能化和加强管理监督的担忧加剧了对预测算法的不满。出现了两种实用的抵抗策略：拖延和数据混淆。最后，我们讨论了预测技术如何不会取代，而是将自由裁量权转移到组织内不那么明显（因此不那么负责）的领域，这一转变对大数据时代的不平等和司法管理具有重要影响。
The number of predictive technologies used in the U.S. criminal justice system is on the rise. Yet there is little research to date on the reception of algorithms in criminal justice institutions. We draw on ethnographic fieldwork conducted within a large urban police department and a midsized criminal court to assess the impact of predictive technologies at different stages of the criminal justice process. We first show that similar arguments are mobilized to justify the adoption of predictive algorithms in law enforcement and criminal courts. In both cases, algorithms are described as more objective and efficient than humans' discretionary judgment. We then study how predictive algorithms are used, documenting similar processes of professional resistance among law enforcement and legal professionals. In both cases, resentment toward predictive algorithms is fueled by fears of deskilling and heightened managerial surveillance. Two practical strategies of resistance emerge: foot-dragging and data obfuscation. We conclude by discussing how predictive technologies do not replace, but rather displace discretion to less visible-and therefore less accountable-areas within organizations, a shift which has important implications for inequality and the administration of justice in the age of big data.