Effects of algorithmic control on power asymmetry and inequality within organizations

Mehdi Barati () and Bahareh Ansari
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Mehdi Barati: University at Albany-State University of New York
Bahareh Ansari: University at Albany

Journal of Management Control: Zeitschrift für Planung und Unternehmenssteuerung, 2022, vol. 33, issue 4, No 3, 525-544

Abstract: Abstract Algorithmic control is expanding in various domains with the advances in programming algorithms, the continuous increase in hardware computing power, larger amounts of available fine-grained data, and an increasing number of organizations exercising remote work. Scholars and practitioners in human resource management posit that organizations’ adoption of algorithms as a substitute for or supplement to traditional rational control mechanisms to direct, discipline, and evaluate workers might increase the objectivity and transparency of worker-related decision-making processes and, therefore, reduce the power asymmetry and inequality within organizations. This discussion commentary argues that the underlying assumptions of the higher objectivity and transparency of algorithms in organizational control are very strong, and current evidence does not support them. There is also evidence of large variation in organizations’ adoption of algorithmic control due to their current technical, structural, and human capital resources, which further blurs the predicted outcomes. Evidence also exists for an over-reliance on algorithmic suggestions by managers to circumvent accountability. Adopting algorithmic control must therefore be conducted with serious precautions. This article proposes that overestimation of objectivity and transparency, and large variation in organizations’ adoption of AC (including the lack of technical and managerial knowledge of the underlying mechanisms of learning algorithms in some organizations, and the complete abandonment of human intuitive judgment and reasoning in others) could worsen the power asymmetry and inequality within organizations by increasing the opacity of decisions, systematic biases, discriminatory classification, and violation of worker privacy.

Keywords: Algorithmic control; Inequality; Power asymmetry; Electronic performance monitoring; Machine learning; Workplace privacy (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s00187-022-00347-6

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