Algorithmic Law: Law Production by Data or Data Production by Law?

María J Catanzariti
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Abstract

Online human interactions are a continuous matching of data that affects both our physical and virtual life. How data are coupled and aggregated is the result of what algorithms constantly do through a sequence of computational steps that transform the input into the output. In particular, machine learning techniques are based on algorithms that identify patterns in datasets. The paper explores how algorithmic rationality may fit into Weber’s conceptualization of legal rationality. It questions the idea that technical disintermediation may achieve the goal of algorithmic neutrality and objective decision-making. It argues that such rationality is represented by surveillance purposes in the broadest meaning. Algorithmic surveillance reduces the complexity of reality calculating the probability that certain facts happen on the basis of repeated actions. Algorithms shape human behaviour, codifying situations and facts, stigmatizing groups rather than individuals, and learning from the past: predictions may lead to static patterns that recall the idea of caste societies, in which the individual potential of change and development is far from being preserved. The persuasive power of algorithms (the so-called nudging) largely consists of small changes aimed at predicting social behaviours that are expected to be repeated in time. This boost in the long run builds a model of antisocial mutation, where actions are oriented. Against such a backdrop, the role of law and legal culture is relevant for individual emancipation and social change in order to frame a model of data production by law. This chapter is divided into four sections: the first part describes commonalities and differences between legal bureaucracy and algorithms, the second part examines the linkage between a datadriven model of law production and algorithmic rationality, the third part shows the different perspective of the socio-legal approach to algorithmic regulation, and the fourth section questions the idea of law production by data as a product of legal culture.
算法法:数据产生法律还是数据产生法律?
在线人际互动是一种持续的数据匹配,影响着我们的现实生活和虚拟生活。数据如何耦合和聚合是算法通过将输入转换为输出的一系列计算步骤不断执行的结果。特别是,机器学习技术是基于识别数据集中模式的算法。本文探讨了算法理性如何与韦伯的法律理性概念相适应。它质疑技术脱媒可能实现算法中立和客观决策目标的想法。它认为,这种合理性在最广泛的意义上表现为监视目的。算法监控降低了计算某些事实在重复行动的基础上发生的概率的复杂性。算法塑造人类行为,将情况和事实编纂成法典,使群体而非个人蒙受耻辱,并从过去吸取教训:预测可能导致静态模式,让人想起种姓社会的想法,在这种社会中,个人的变化和发展潜力远远没有得到保护。算法的说服力(所谓的“助推”)主要由旨在预测有望在时间上重复的社会行为的微小变化组成。从长远来看,这种促进建立了一个反社会突变的模型,在这个模型中,行为是有导向的。在这样的背景下,法律和法律文化的作用与个人解放和社会变革息息相关,以便构建法律生产数据的模式。本章分为四个部分:第一部分描述了法律官僚主义与算法之间的共同点和差异,第二部分考察了数据驱动的法律生产模式与算法理性之间的联系,第三部分展示了社会-法律方法对算法监管的不同视角,第四部分质疑了作为法律文化产物的数据法律生产理念。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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