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<p>In a world increasingly shaped by algorithmic decision-making, Shin’s <i>Debiasing AI: Rethinking the Intersection of Innovation and Sustainability</i> emerges as a timely and ambitious contribution, offering a crucial interdisciplinary lens through which to examine the systemic biases ingrained within artificial intelligence (AI). The book proactively suggests a fundamental rethinking of our algorithmic age, delving into the ontological, phenomenological, and epistemological underpinnings of these biases, alongside the critical governance of AI ethics. This organization transforms the book into a comprehensive guide encompassing both the conceptual underpinnings and practical applications aimed at achieving fairer and more equitable algorithmic decision-making through the mitigation of systemic AI biases. Drawing strength from communication studies, cognitive science, and information systems, Shin presents a compelling critique of purely techno-solutionist approaches, advocating for a more nuanced and human-centered alternative. Each chapter thoughtfully interweaves literature synthesis, theoretical critique, and insightful scenario-based reflections. Building upon this comprehensive overview of the book’s foundational approach, Shin then delves into the crucial ethical considerations that underpin the challenge of algorithmic bias.</p>

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Debiasing AI: Rethinking the intersection of innovation and sustainability, Donghee Shin

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  • 发表日期：

    2025年6月7日

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摘要

In a world increasingly shaped by algorithmic decision-making, Shin’s Debiasing AI: Rethinking the Intersection of Innovation and Sustainability emerges as a timely and ambitious contribution, offering a crucial interdisciplinary lens through which to examine the systemic biases ingrained within artificial intelligence (AI). The book proactively suggests a fundamental rethinking of our algorithmic age, delving into the ontological, phenomenological, and epistemological underpinnings of these biases, alongside the critical governance of AI ethics. This organization transforms the book into a comprehensive guide encompassing both the conceptual underpinnings and practical applications aimed at achieving fairer and more equitable algorithmic decision-making through the mitigation of systemic AI biases. Drawing strength from communication studies, cognitive science, and information systems, Shin presents a compelling critique of purely techno-solutionist approaches, advocating for a more nuanced and human-centered alternative. Each chapter thoughtfully interweaves literature synthesis, theoretical critique, and insightful scenario-based reflections. Building upon this comprehensive overview of the book’s foundational approach, Shin then delves into the crucial ethical considerations that underpin the challenge of algorithmic bias.