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Why do social computing projects aimed at alleviating social inequality fail? This paper investigates this question through a qualitative interview study with 25 individuals working to address the problem of wage theft in the United States (US) context. Our analyses uncover failures at three levels or scales of interaction: one, failures at the individual level of technology adoption; two, relational failures (i.e., the anti-labor worker/employer dynamic in the US); and three, institutional or macro-level failures. Taken together, these various failings point to larger, structural forces that negatively fate pro-labor projects’ trajectories – i.e., capitalism. Capitalism's incarnations in the US play a significant and at times harsh grip in steering the path of social computing design projects. In this paper, we untangle the relationship between capitalism and social computing, providing an analytic framework to tease apart this complex relationship, the lessons learned from our empirical data, as well as ways forward for future, pro-labor, social computing projects. 

The rapid development of machine-learning algorithms, which underpin contemporary artificial intelligence systems, has created new opportunities for the automation of work processes and management functions. While algorithmic management has been observed primarily within the platform-mediated gig economy, its transformative reach and consequences are also spreading to more standard work settings. Exploring algorithmic management as a sociotechnical concept, which reflects both technological infrastructures and organizational choices, we discuss how algorithmic management may influence existing power and social structures within organizations. We identify three key issues. First, we explore how algorithmic management shapes pre-existing power dynamics between workers and managers. Second, we discuss how algorithmic management demands new roles and competencies while also fostering oppositional attitudes toward algorithms. Third, we explain how algorithmic management impacts knowledge and information exchange within an organization, unpacking the concept of opacity on both a technical and organizational level. We conclude by situating this piece in broader discussions on the future of work, accountability, and identifying future research steps.