Extending the framework of algorithmic regulation: The Uber case

Autor:	Eyert, Florian, Irgmaier, Florian, Ulbricht, Lena
Rok vydání:	2020
Předmět:	Technik Technologie Technology (Applied sciences) algorithmic regulation big data quantification Technikfolgenabschätzung Technology Assessment Regulierung Algorithmus künstliche Intelligenz Automatisierung Entscheidungsfindung regulation algorithm artificial intelligence automation decision making
Zdroj:	Regulation & Governance, Early View Articles
Druh dokumentu:	Zeitschriftenartikel<br />journal article
ISSN:	1748-5991
DOI:	10.1111/rego.12371
Popis:	In this article, we take forward recent initiatives to assess regulation based on contemporary computer technologies such as big data and artificial intelligence. In order to characterize current phenomena of regulation in the digital age, we build on Karen Yeung's concept of "algorithmic regulation," extending it by building bridges to the fields of quantification, classification, and evaluation research, as well as to science and technology studies. This allows us to develop a more fine‐grained conceptual framework that analyzes the three components of algorithmic regulation as representation, direction, and intervention and proposes subdimensions for each. Based on a case study of the algorithmic regulation of Uber drivers, we show the usefulness of the framework for assessing regulation in the digital age and as a starting point for critique and alternative models of algorithmic regulation.
Databáze:	SSOAR – Social Science Open Access Repository
Externí odkaz:	https://www.ssoar.info/ssoar/handle/document/76637 Zobrazit plný text záznamu Plný text ve formátu PDF Plný text ve formátu HTML
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