Algorithmic management in a work context

The rapid development of machine-learning algorithms, which underpin contemporary artificial intelligence systems, has created new opportunities for the automation of work processes and management functions. While algorithmic management has been observed primarily within the platform-mediated gig ec...

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Vydané v:Big data & society Ročník 8; číslo 2
Hlavní autori: Jarrahi, Mohammad Hossein, Newlands, Gemma, Lee, Min Kyung, Wolf, Christine T., Kinder, Eliscia, Sutherland, Will
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: London, England SAGE Publications 01.07.2021
Sage Publications Ltd
SAGE Publishing
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ISSN:2053-9517, 2053-9517
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Shrnutí:The rapid development of machine-learning algorithms, which underpin contemporary artificial intelligence systems, has created new opportunities for the automation of work processes and management functions. While algorithmic management has been observed primarily within the platform-mediated gig economy, its transformative reach and consequences are also spreading to more standard work settings. Exploring algorithmic management as a sociotechnical concept, which reflects both technological infrastructures and organizational choices, we discuss how algorithmic management may influence existing power and social structures within organizations. We identify three key issues. First, we explore how algorithmic management shapes pre-existing power dynamics between workers and managers. Second, we discuss how algorithmic management demands new roles and competencies while also fostering oppositional attitudes toward algorithms. Third, we explain how algorithmic management impacts knowledge and information exchange within an organization, unpacking the concept of opacity on both a technical and organizational level. We conclude by situating this piece in broader discussions on the future of work, accountability, and identifying future research steps.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:2053-9517
2053-9517
DOI:10.1177/20539517211020332