Effects of algorithmic control on power asymmetry and inequality within organizations

Mehdi Barati*, Bahareh Ansari

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)
41 Downloads (Pure)


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.
Original languageEnglish
Pages (from-to)525–544
Number of pages20
JournalJournal of Management Control
Early online date17 Oct 2022
Publication statusPublished - Dec 2022
Externally publishedYes


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