ENVENV-003 — Resource Allocation Imbalances
AI Training and Infrastructure Lifecycle Causes Systemic Environmental Harm
4/5Sector: EnergyGeography: GlobalStage: OperateIngested: —
Executive Summary
AI systems impose material environmental costs across their full lifecycle, from resource extraction through energy-intensive training to toxic e-waste disposal. Boards without visibility into these harms face mounting regulatory, reputational, and supply-chain risk.
Domain
Environmental Factors
Blindspots in organisational culture, stakeholder expectations, resource allocation, market pressure, regulatory environment, and external partnerships.
Source
MIT AI Risk Repository — Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reduction (Shelby2023) ↗https://airisk.mit.edu/
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