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The nexus between technology and workplace inequality has been a long-standing topic of scholarly interest, now heightened by the rapid evolution of artificial intelligence (AI). Our review moves beyond dystopian or utopian views of AI by identifying four perspectives—normative, cognitive, structural, and relational—espoused by scholars examining the impact of AI on workplace inequality specifically, and the structure and organization of work more broadly. We discuss the respective strengths, limitations, and underlying assumptions of these perspectives and highlight how each perspective speaks to a particular facet of workplace inequality: either encoded, evaluative, wage, or relational inequality. Integrating these perspectives enables a deeper understanding of the mechanisms, processes, and trajectories through which AI influences workplace inequality, as well as the role that organizational managers, workers, and policymakers could play in the process. Toward this end, we introduce a framework on the “inequality cascades” of AI that traces how and when inequality emerges and amplifies cumulatively as AI systems progress through the phases of development, implementation, and use in organizations. In turn, we articulate a research agenda for management and organizational scholars to better understand AI and its multifaceted impact on workplace inequality, and we examine potential mechanisms to mitigate its adverse consequences. 

Research on multisided platforms has emphasized how platform owners accumulate significant power over other platform actors, such as producers and customers, arguing for the need to balance such power with accountability. We review two perspectives on platform accountability: (a) a bottom-up, emergent perspective that focuses on the collective action taken by lower-powered platform actors such as producers (e.g., gig workers, app developers) to enhance rule adequacy and push back against platform owners’ power; and (b) a top-down, institutional perspective that emphasizes preventing extractive opportunism and maintaining a level playing field among different platform actors by enabling legal, regulatory, and governance changes. The bottom-up perspective’s overarching focus is on procedural (rule-focused) fairness, while the top-down perspective’s focus is largely on distributive (outcome-focused) fairness. While both perspectives are important, they have limitations regarding platform accountability, especially given the power and informational asymmetries inherent among platformactors. Therefore, synthesizing across literatures, we provide a framework for platform accountability that accounts for both procedural and distributive fairness, and is based on a fundamental premise: multisided platforms require multisided accountability systems. Thus, our review proposes an approach for enforcing platform accountability that has the potential to rebalance the power between high-powered and low-powered platform actors.