The Hidden Costs of AI: A Review of Energy, E-Waste, and Inequality in Model Development

AI training consumes significant energy with substantial carbon emissions: The paper identifies high energy consumption from model training as a critical environmental concern, contributing to AI's carbon footprint in ways that often go unrecognized when evaluating AI systems' overall impact.

Rapid hardware turnover creates a growing e-waste problem: As AI development accelerates, the frequent replacement of computing hardware generates electronic waste at scale, raising concerns about resource depletion and environmental contamination that deserve greater attention in sustainability discussions.

Access to computing power is unequally distributed globally: The review highlights systemic disparities in who can afford and access the computational resources needed for AI development, potentially concentrating AI innovation and benefits among wealthy nations and organizations while excluding others from participation.

Cybersecurity for AI systems carries hidden energy costs: Beyond model training itself, the paper reveals that securing AI infrastructure requires substantial energy, representing an often-overlooked component of AI's total environmental burden that should be factored into responsible development practices.

Current AI progress metrics ignore environmental and ethical dimensions: The paper argues that measuring AI success solely through performance improvements obscures critical issues around sustainability, equity, and transparency, suggesting the field needs more holistic evaluation frameworks aligned with responsible AI principles.