Algorithmic Status Inequality: An Integrative Perspective on AI‐Driven Social Stratification - Wu, Jie (Journal of Management Studies, 2026)
Abstract This Point introduces algorithmic status inequality that is, enduring disparities in social position, influence, and resource access reinforced by AI systems, as a critical lens for understanding technological stratification in organizations. I develop an integrative model showing how computational beliefs (i.e., cultural assumptions embedded in algorithmic design) interact with computational inequalities (i.e., disparities in technical capabilities) to produce persistent status hierarchies through self‐reinforcing feedback loops. Illustrative cases in recruitment, healthcare and legal assistance demonstrate these mechanisms in practice, producing three consequential patterns: status schisms within minority‐serving AI systems, status tensions between dominant and specialized algorithms and status disparities between AI systems and human experts. Unlike the Counterpoint perspectives that either emphasize market‐based dynamic capabilities as self‐correcting mechanisms for algorithmic disparities or locate the primary source of inequality in biased training data amenable to technical debiasing , I argue that neither purely technical approaches (such as diversifying training data) nor purely market‐based approaches (such as relying on competitive dynamics) are sufficient to address algorithmic status inequality. This sociotechnical perspective explains why isolated interventions typically fail and provides a theoretical foundation for developing comprehensive strategies that simultaneously target embedded cultural beliefs and material inequalities in AI development.