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<p>As artificial intelligence (AI) systems expand rapidly across sectors, concerns about equitable participation and benefit-sharing have grown increasingly urgent. While the global discourse has emphasized data ethics and algorithmic bias, less attention has been given to the <i>structural mechanisms</i> through which AI development and adoption may reinforce exclusion. This paper introduces a Systems Thinking framework to conceptualize and analyze the emerging “AI inclusivity gap”, defined as the unequal ability of institutions, regions, and populations to engage in the creation, governance, and meaningful use of AI. Drawing on literature from 2020 to 2025, the study constructs a causal-loop and stock-and-flow model that maps dynamic feedback loops across AI education, infrastructure, policy, and epistemic legitimacy. The model surfaces key leverage points, such as inclusive curriculum design, participatory governance, and decentralized compute access, that can shift systemic trajectories toward more equitable outcomes. Illustrative case examples from healthcare, language technology, and agriculture demonstrate both risks and opportunities in applying AI for inclusion. The paper argues that inclusion must be treated as an upstream system design principle, not a downstream ethical add-on. It offers a set of actionable recommendations and proposed metrics to guide institutional, governmental, and global AI strategies. By reframing AI exclusion as a systemic and dynamic phenomenon, the study provides a conceptual foundation for future empirical validation, policy modeling, and participatory design interventions.</p>

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Rethinking AI inclusion: a systems thinking framework for policy, education, and participation

  • 期刊论文

  • 发表日期：

    2025年8月18日

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  • Arkapravo Sarkar

摘要

As artificial intelligence (AI) systems expand rapidly across sectors, concerns about equitable participation and benefit-sharing have grown increasingly urgent. While the global discourse has emphasized data ethics and algorithmic bias, less attention has been given to the structural mechanisms through which AI development and adoption may reinforce exclusion. This paper introduces a Systems Thinking framework to conceptualize and analyze the emerging “AI inclusivity gap”, defined as the unequal ability of institutions, regions, and populations to engage in the creation, governance, and meaningful use of AI. Drawing on literature from 2020 to 2025, the study constructs a causal-loop and stock-and-flow model that maps dynamic feedback loops across AI education, infrastructure, policy, and epistemic legitimacy. The model surfaces key leverage points, such as inclusive curriculum design, participatory governance, and decentralized compute access, that can shift systemic trajectories toward more equitable outcomes. Illustrative case examples from healthcare, language technology, and agriculture demonstrate both risks and opportunities in applying AI for inclusion. The paper argues that inclusion must be treated as an upstream system design principle, not a downstream ethical add-on. It offers a set of actionable recommendations and proposed metrics to guide institutional, governmental, and global AI strategies. By reframing AI exclusion as a systemic and dynamic phenomenon, the study provides a conceptual foundation for future empirical validation, policy modeling, and participatory design interventions.