The Algorithmic Workforce: A Qualitative Study on the Impact of AI-Driven Management on Labor Autonomy in the Gig Economy
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Background. The rapid expansion of AI-driven management systems in the gig economy has transformed labor processes, reshaping how autonomy, decision-making, and work conditions are structured. This study addresses the growing concern that algorithmic oversight may simultaneously enhance efficiency while constraining workers’ control over their tasks and mobility.
Purpose. The research aims to examine how gig workers interpret, negotiate, and respond to algorithmic management practices embedded in digital labor platforms.
Method. Using a qualitative design, the study draws on in-depth interviews and digital ethnography involving ride-hailing and delivery workers across multiple urban regions.
Results. The findings reveal a dualistic impact: algorithms streamline workflow coordination and reduce transactional ambiguity, yet they also introduce opaque decision rules, performance scoring pressures, and subtle forms of behavioral nudging that weaken workers’ perceived autonomy. Participants reported adaptive strategies, such as system gaming and collective knowledge sharing, to counterbalance algorithmic constraints.
Conclusion. The study concludes that AI-driven management does not uniformly diminish autonomy but reconfigures it through dynamic tensions between control, dependency, and worker agency. These insights underscore the need for regulatory frameworks and platform design principles that ensure transparency, fairness, and human-centered governance in algorithmic workplaces.
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