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

TL;DR

This review highlights the overlooked environmental and ethical challenges of AI, including energy use, e-waste, inequality, and cybersecurity, emphasizing the need for sustainable and responsible AI development.

Contribution

It identifies systemic issues and research gaps related to AI's environmental and ethical impacts, advocating for sustainable, transparent, and equitable practices.

Findings

High emissions from model training

Rising hardware turnover and e-waste

Disparities in global compute access

Abstract

Artificial intelligence (AI) has made remarkable progress in recent years, yet its rapid expansion brings overlooked environmental and ethical challenges. This review explores four critical areas where AI's impact extends beyond performance: energy consumption, electronic waste (e-waste), inequality in compute access, and the hidden energy burden of cybersecurity systems. Drawing from recent studies and institutional reports, the paper highlights systemic issues such as high emissions from model training, rising hardware turnover, global infrastructure disparities, and the energy demands of securing AI. By connecting these concerns, the review contributes to Responsible AI discourse by identifying key research gaps and advocating for sustainable, transparent, and equitable development practices. Ultimately, it argues that AI's progress must align with ethical responsibility and environmental stewardship to ensure a more inclusive and sustainable technological future.