Unsustainable AI (Confronting the Environmental and Societal Costs of Artificial Intelligence)

AI's true environmental and societal costs exposed with an actionable sustainability framework

Unsustainable AI: Confronting the Environmental and Societal Costs of Artificial Intelligence traces the full environmental and social footprint of artificial intelligence, from its massive energy consumption and freshwater demands to its rising carbon emissions. Written by Ahmad Antar, founder of the non-profit Digital Emissions and instructor at Harvard Extension School, this narrative-driven investigation also examines algorithmic bias, job displacement, digital repression, and privacy erosion, building an evidence-based case that AI's current trajectory is structurally unsustainable.

Beyond diagnosis, the book concludes with a concrete, actionable framework and roadmap for implementing sustainable AI practices, moving readers from awareness of AI's harms to responsible, measurable action. It equips readers with the language and evidence to evaluate AI sustainability claims on their own terms, exposing how industry pledges are failing to keep pace with AI's rapid expansion across energy, water, and social domains.

Readers will also find:

• An examination of AI's energy hunger, freshwater consumption, e-waste, and pollution across the technology's full operational lifecycle
• Analysis of AI's social justice impacts including algorithmic bias, digital divide, security, disinformation, the future of jobs, digital repression, AI warfare, and mental health
• A concrete roadmap designed for IT leaders and executives to implement sustainable, responsible AI practices within their organizations
• Frameworks originally tested in the first university-level sustainable AI course offered at any American academic institution

Written for policymakers, business leaders, technology executives, and general readers concerned about AI's unchecked growth, this book provides both the evidence to understand AI's environmental and societal costs and a practical framework for moving toward responsible, sustainable AI implementation in organizational and policy settings.

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