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Automation

Type
book section
Date Issued
2022-11-09
Author(s)
Simmler, Monika  
Editor(s)
Caeiro, Pedro
Gless, Sabine
Mitsilegas, Valsamis
João Costa, Miguel
De Snaijer, Janneke
Theodorakakou, Georgia
Abstract
Automation generally refers to a state in which a machine performs tasks or parts of a task formerly executed by humans. The extent to which a task is taken over by a machine varies, resulting in different levels of automation. The greater the level of automation, the less control remains with the human operator. Thus, automation means delegating action. If such delegation or the performance of the machine results in criminal wrongdoing, a question arises as to whom the machine’s ‘acts’ can be legally ascribed. Automation brings multiple challenges for criminal law since the attribution of acts to an individual lays the groundwork for criminal liability. This entry outlines and discusses the consequences of automation for criminal law. As a first step, the questions if and how automation shifts responsibility and coins attribution processes are considered theoretically and on the grounds of empirical findings (II.). As a second step, the conditions of criminal liability for intentional and negligent wrongdoing by technical systems (i.e., as a result of automation) are discussed, elaborating upon the normative expectations criminal law directs toward individuals and corporations in charge of automation (III.). Finally, prospects are outlined that can serve as a basis for further development of criminal law in the context of automation (IV.).
Language
English
HSG Classification
contribution to scientific community
HSG Profile Area
LS - Business Enterprise - Law, Innovation and Risk
Book title
Elgar Encyclopedia of Crime and Criminal Justice
Publisher
Edward Elgar Publishing
Official URL
https://www.elgaronline.com/view/book/9781789902990/b-9781789902990.automation.xml?tab_body=fulltext-copy1
URL
https://www.alexandria.unisg.ch/handle/20.500.14171/108094
Subject(s)

law

Division(s)

LS - Law School

Eprints ID
267891

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