Algorithmic Management in Digital Platforms and Work Dehumanization in the Gig Economy
Keywords:
algorithmic management, digital platforms, gig economy, dehumanization, autonomy, wellbeing, procedural fairnessAbstract
This paper examines algorithmic management within digital labor platforms and the gig economy, focusing on how automated allocation, evaluation, and enforcement shape worker autonomy and wellbeing. The synthesis clarifies dehumanization as a process in which workers are treated as data points, rated objects, and replaceable inputs rather than deliberating agents. Algorithmic control operates through opaque scoring, dynamic pricing, surveillance by apps, and incentive structures that steer behavior while limiting meaningful voice. These mechanisms can narrow discretion, intensify time pressure, and shift operational risks to workers through variable demand and unilateral rule changes. The paper discusses how information asymmetry, restricted contestability of decisions, and constant performance visibility foster stress, self monitoring, and reputational insecurity. It also outlines conditions that may temper harmful outcomes, including transparent criteria, accessible appeal channels, collective representation, and human oversight tied to procedural fairness. The paper concludes that platform governance should be assessed as a labor regulation regime, where technological design choices materially shape dignity, autonomy, and social protection for contingent workers. It recommends framing autonomy as negotiated discretion and wellbeing as sustained capacity, linking them to accountability, explainability, and enforceable rights at work.
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