The Hidden Costs of AI: A Review of Energy, E-Waste, and Inequality in Model Development
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 6 of its 15 references that are in the map. First posted on arXiv 13 Jul 2025.
Builds on
6 of 15 references.
References · 6
- 2019Energy and Policy Considerations for Deep Learning in NLPcited by 1,254
- 2019Green AIcited by 627
- 2021Carbon Emissions and Large Neural Network Trainingcited by 567
- 2020Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claimscited by 165
- 2020Decolonial AI: Decolonial Theory as Sociotechnical Foresight in Artificial Intelligencecited by 101
- 2020The De-democratization of AI: Deep Learning and the Compute Divide in Artificial Intelligence Researchcited by 67
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Citing papers per year: 2025: 2.
2 papers.