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<p>This paper proposes <i>A People’s AI</i> as a framework that moves beyond prevailing approaches to AI democratization. While contemporary initiatives emphasize expanding access and participation, they often remain fragmented and reproduce existing power asymmetries by treating democratization as a contingent provision or add-on to business as usual. Western regulatory efforts have likewise fallen short of establishing meaningful democratization, since corporate lobbying has constrained their capacity to secure local control or redistribute authority over knowledge production. <i>A People’s AI</i> advances four core principles: (1) the elevation of local knowledge, which challenges the primacy of technical expertise; (2) meaningful local control across the AI lifecycle; (3) collectivity, which recognizes that AI systems shape communal rather than individual outcomes; and (4) reflexive grounding that requires ongoing accountability through continuous communal evaluation. Drawing on critiques of technological determinism and Cartesian dualism, this framework reconceptualizes AI as a human expression rather than an autonomous technology. This shift grounds the legitimacy of <i>A People’s AI</i> in collective oversight, continuous consent, and the enduring capacity for communal refusal. The paper then illustrates how these principles can be enacted in real-world contexts to advance cultural preservation, social equity, and collective self-determination beyond immediate political interests.</p>

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A People’s AI: Beyond Participation, Toward Genuine Democratization

  • 期刊论文

  • 发表日期：

    2026年5月23日

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  • John W. Murphy,
  • Randon R. Taylor,
  • Marco Antonio Lueras

摘要

This paper proposes A People’s AI as a framework that moves beyond prevailing approaches to AI democratization. While contemporary initiatives emphasize expanding access and participation, they often remain fragmented and reproduce existing power asymmetries by treating democratization as a contingent provision or add-on to business as usual. Western regulatory efforts have likewise fallen short of establishing meaningful democratization, since corporate lobbying has constrained their capacity to secure local control or redistribute authority over knowledge production. A People’s AI advances four core principles: (1) the elevation of local knowledge, which challenges the primacy of technical expertise; (2) meaningful local control across the AI lifecycle; (3) collectivity, which recognizes that AI systems shape communal rather than individual outcomes; and (4) reflexive grounding that requires ongoing accountability through continuous communal evaluation. Drawing on critiques of technological determinism and Cartesian dualism, this framework reconceptualizes AI as a human expression rather than an autonomous technology. This shift grounds the legitimacy of A People’s AI in collective oversight, continuous consent, and the enduring capacity for communal refusal. The paper then illustrates how these principles can be enacted in real-world contexts to advance cultural preservation, social equity, and collective self-determination beyond immediate political interests.