AI发展隐含能耗、电子垃圾与不平等，亟需可持续设计
The Hidden Costs of AI: A Review of Energy, E-Waste, and Inequality in Model Development
- 分析模型训练能耗、硬件淘汰率与算力资源分配不均
- 指出全球算力基础设施存在显著区域差异
- 呼吁推动绿色、公平的AI研发体系
人工智能近年取得显著进展，但其快速扩张带来被忽视的环境与伦理挑战。本综述探讨四个关键领域：模型训练的高能耗、硬件更新导致的电子垃圾增加、算力获取的不平等，以及网络安全系统的隐性能源负担。基于最新研究与机构报告，文章揭示了系统性问题，如训练过程中的高碳排放、硬件淘汰周期缩短、全球基础设施差距扩大，以及安全防护带来的额外能耗。通过整合这些议题，综述为负责任的人工智能发展提供依据，识别关键研究空白，并倡导更可持续、透明与公平的研发实践。最终强调，人工智能进步必须与伦理责任和环境关怀相协调，以实现更具包容性与可持续性的技术未来。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。