Algorithmic management in a work context

By Mohammad Hossein Jarrahi, G. Newlands, Min Kyung Lee, Christine T. Wolf, Eliscia Kinder, Will Sutherland. Big Data & Society, 2021

363 citations recorded by the source corpus.

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.

Subfields: Information Systems, Safety Research, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/4c2705ee5a75d6ecadbb53df07d008fd73e98ad9

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Information Systems

1. Divya Ramesh
2. Patrick Hall
3. Ding Wang
4. Nithya Sambasivan
5. Vaishnav Kameswaran

In Safety Research

1. Divya Ramesh
2. Patrick Hall
3. Ding Wang
4. Nithya Sambasivan
5. Vaishnav Kameswaran

In Sociology and Political Science

1. Divya Ramesh
2. Ding Wang
3. Nithya Sambasivan
4. Vaishnav Kameswaran

Citations in the corpus

• Towards a Standard for Identifying and Managing Bias in Artificial Intelligence (2022, background): “This technology is also often presented and perceived as a way to reduce human cognitive biases and make decisions more fair and objective [27, 181, 182].”
• How Platform-User Power Relations Shape Algorithmic Accountability: A Case Study of Instant Loan Platforms and Financially Stressed Users in India (2022, background): “Organizationallevel changes from technology providers most often occur as a result of regulatory and user pressures [103], low-powered users may find it challenging to regain their agency displaced by platforms [69, 81], and mechanisms may have limited efficacy where there is power asymmetry [17, 76”