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Demonstrates that AI-related exclusion impacts psychological well-being and reveals key inequalities in society.
Artificial intelligence (AI) has become a foundational component of contemporary social, economic, and political life. Yet, the ways in which AI reshapes patterns of exclusion beyond questions of access and technical capability remain insufficiently theorized. This article argues that the AI divide is best understood as a multidimensional sociotechnical phenomenon, in which technological inequality is intertwined with psychological, institutional, and geopolitical dynamics that shape both participation and agency. Drawing on digital inequality studies, philosophy of technology, and psychology, and supported by a synthesis of recent empirical findings, the article conceptualizes the AI divide across three interrelated dimensions—access, use, and outcomes—while foregrounding the role of power asymmetries, opaque governance structures, and market concentration in producing unequal participation in AI‐mediated systems. Building on this structural framework, the analysis advances a second contribution by integrating the psychological consequences of AI‐related exclusion: algorithmic anxiety, diminished self‐efficacy, technostress, and collective vulnerability, operating across individual, organizational, and societal levels. This study demonstrates that these psychological effects are not merely secondary consequences but actively reproduce and intensify technological inequality by weakening agency, undermining trust, and constraining sociotechnical imaginaries. These dynamics are better understood as contingent on governance, design, and patterns of use rather than as inevitable outcomes of AI deployment. By linking structural conditions with psychological experience, the study underscores the normative importance of transparency, explicability, and participatory governance as prerequisites for more inclusive and human‐centered AI systems. Finally, the paper outlines concrete and operational policy recommendations, including algorithmic impact assessments and regulatory sandboxes, to mitigate these multidimensional inequalities.
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Christos Papaioannou (2026) studied this question.
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