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Artificial Intelligence for Democratic Innovation? Blind Spots from a Structural Perspective

Democracy
Political Theory
Qualitative
Quantitative
Technology
Sammy Mckinney
University of Cambridge
Sammy Mckinney
University of Cambridge

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Abstract

That Artificial Intelligence (AI) brings opportunities and risks to the practice of democratic innovation is widely remarked in both scholarship and practice: from rapid data transformation and improved deliberative quality, on one hand, to algorithmic bias and technological solutionism, on the other, the role of AI in democratic practice is of increasing interest to scholars and practitioners alike. However, to date, much of this analysis has focused on the instrumental gains that AI integration brings to democratic innovations as discrete sites of participation, such as AI’s capacity to bring deliberation to more people (Landemore, 2023), support consensus generation (Tessler et al., 2024), promote reflection at elections (Fishkin et al., 2025), improve facilitation practices (Jigsaw, 2025), or strike a balance between advancing democratic values whilst promoting institutional capacity (McKinney, 2024). Whilst these are all essential axes of analysis, this article contends that this work has a significant blind spot: it fails to attend to the integration of AI from a structural perspective. A structural perspective demands that we do not simply attend to the way AI instrumentally affects the quality of isolated democratic forums, but rather how AI integration externalises harms, transforms path dependencies and introduces political vulnerabilities that may hinder the ability of such processes to meaningfully contribute to democratic change-making. This structural perspective requires situating the analysis of AI’s role in democratic innovation against a wider backdrop, including questions of political economy, the materiality of democracy, and the construction of technological imaginaries. To do so, this paper merges deliberative democracy and critical data studies scholarship with novel qualitative and quantitative empirical data to identify significant blind spots in current discourse around AI and the future of democratic innovation.