{"title":"Algorithmic Law: Law Production by Data or Data Production by Law?","authors":"María J Catanzariti","doi":"10.1017/9781108914857.006","DOIUrl":null,"url":null,"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.","PeriodicalId":183658,"journal":{"name":"Constitutional Challenges in the Algorithmic Society","volume":"46 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Constitutional Challenges in the Algorithmic Society","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1017/9781108914857.006","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
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.