Making sense of the algorithm

This study explores how on-location platform workers perceive and interpret algorithmic management within digitally mediated labor environments. While platforms often present their algorithmic systems as neutral, objective, and designed for efficiency, workers frequently encounter them as opaque, inconsistent, and lacking in transparency. In the absence of clear communication about how decisions, such as task allocation, performance ratings, and disciplinary measures, are made, workers engage in a process of sensemaking and develop informal theories to explain how the system functions. These interpretations play a key role in shaping workers’ daily strategies and behaviors. Perceptions about what actions are rewarded or punished, such as logging in at certain times, consistently accepting tasks, or demonstrating specific behavioral patterns, influence how workers organize their availability, pace their work, and try to align with platform expectations. Even when these assumptions are speculative, they nonetheless produce real behavioral effects. In this way, algorithmic ambiguity becomes a powerful mechanism of indirect control. Workers begin to self-discipline based on presumed algorithmic preferences, even in the absence of formal rules or explicit feedback. This dynamic reinforces power asymmetries, as the platform retains control over decision making while minimizing accountability and managerial visibility. Drawing on existing empirical literature, this study argues that the power of algorithmic management lies not only in its ability to automate coordination and oversight, but also in the psychological and behavioral effects it produces through uncertainty. By centering the interpretations and practices of workers, the paper offers insight into the lived experience of platform work and the subtle, often invisible mechanisms through which control is exercised. This perspective contributes to a more nuanced understanding of algorithmic governance in the platform economy, emphasizing that platform power operates as much through worker perception as it does through code. Our study also highlights the need for further research into how such systems shape worker agency, motivation, and well-being.