Position: Neglecting the Sustainability of AI is Fuelling a Global AI Arms Race
Not on the map. Why?
Abstract (arXiv)
Sustainability encompasses three key facets: economic, environmental, and social. However, the nascent discourse on sustainable artificial intelligence (AI) predominantly focuses on the environmental sustainability of AI, neglecting the economic and social aspects. Achieving truly sustainable AI necessitates addressing the tension between its environmental sustainability, which emphasises mitigating AI's climate impact, and its social sustainability, hinging on equitable access to AI development resources. This push for increased accessibility, however, often overlooks the environmental costs of expanding such resource usage. This position paper argues that reconciling climate awareness and resource awareness is essential to realising truly sustainable AI, and neglecting these factors fuels a global AI arms race. Applying Karl Marx's base-superstructure framework from historical materialism, we analyse how the material conditions are shaping the current AI progress and the discourse surrounding it. Further, we introduce the Climate and Resource Aware Machine Learning (CARAML) framework with actionable recommendations spanning individual, community, industry, government, and global levels to achieve sustainable AI.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 50 of its 132 references that are in the map. First posted on arXiv 27 Feb 2025.
Builds on
50 of 132 references.
References · 50
- 2020Language Models are Few-Shot Learnerscited by 32,199
- 2020Denoising Diffusion Probabilistic Modelscited by 20,851
- 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
- 2023GPT-4 Technical Reportcited by 18,132
- 2024The Llama 3 Herd of Modelscited by 14,192
- 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
- 2023Segment Anythingcited by 8,158
- 2019DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lightercited by 4,243
- 2021On the Opportunities and Risks of Foundation Modelscited by 3,961
- 2016Deep Learning with Differential Privacycited by 2,682
- 2019Energy and Policy Considerations for Deep Learning in NLPcited by 1,242
- 2018Model Cards for Model Reportingcited by 1,068
38 more
- 2014Deep Learning in Neural Networks: An Overviewcited by 765
- 2024Phi-4 Technical Reportcited by 675
- 2023The Falcon Series of Open Language Modelscited by 526
- 2019Quantifying the Carbon Emissions of Machine Learningcited by 516
- 2017Efficient Processing of Deep Neural Networks: A Tutorial and Surveycited by 427
- 20218-bit Optimizers via Block-wise Quantizationcited by 395
- 2022Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Modelcited by 264
- 2020Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learningcited by 246
- 2023TPU v4: An Optically Reconfigurable Supercomputer for Machine Learning with Hardware Support for Embeddingscited by 198
- 2022The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrinkcited by 183
- 2020Characterising Bias in Compressed Modelscited by 162
- 2022Compute Trends Across Three Eras of Machine Learningcited by 161
- 2019The role of artificial intelligence in achieving the Sustainable Development Goalscited by 100
- 2018The Measure and Mismeasure of Fairnesscited by 94
- 2020The De-democratization of AI: Deep Learning and the Compute Divide in Artificial Intelligence Researchcited by 67
- 2022Differentially Private Optimization on Large Model at Small Costcited by 65
- 2023Counting Carbon: A Survey of Factors Influencing the Emissions of Machine Learningcited by 42
- 2022Compute-Efficient Deep Learning: Algorithmic Trends and Opportunitiescited by 38
- 2024Computing Power and the Governance of Artificial Intelligencecited by 38
- 2020Analyzing and Mitigating Data Stalls in DNN Trainingcited by 35
- 2025From Efficiency Gains to Rebound Effects: The Problem of Jevons' Paradox in AI's Polarized Environmental Debatecited by 23
- 2022Carbon Footprint of Selecting and Training Deep Learning Models for Medical Image Analysiscited by 18
- 2022Accurate Fairness: Improving Individual Fairness without Trading Accuracycited by 16
- 2022tf.data service: A Case for Disaggregating ML Input Data Processingcited by 15
- 2022The Effect of Model Compression on Fairness in Facial Expression Recognitioncited by 13
- 2023Efficiency is Not Enough: A Critical Perspective of Environmentally Sustainable AIcited by 12
- 2021Frugal Machine Learningcited by 11
- 2022EC-NAS: Energy Consumption Aware Tabular Benchmarks for Neural Architecture Searchcited by 11
- 2021Horizontally Fused Training Array: An Effective Hardware Utilization Squeezer for Training Novel Deep Learning Modelscited by 7
- 2023On the Limitations of Carbon-Aware Temporal and Spatial Workload Shifting in the Cloudcited by 7
- 2022The Dirty Secret of SSDs: Embodied Carboncited by 6
- 2022An Analysis of Collocation on GPUs for Deep Learning Trainingcited by 6
- 2023Reaching the Edge of the Edge: Image Analysis in Spacecited by 4
- 2023Is TinyML Sustainable? Assessing the Environmental Impacts of Machine Learning on Microcontrollerscited by 4
- 2024PePR: Performance Per Resource Unit as a Metric to Promote Small-Scale Deep Learning in Medical Image Analysiscited by 4
- 2024Position: Cracking the Code of Cascading Disparity Towards Marginalized Communitiescited by 4
- 2024TensorSocket: Shared Data Loading for Deep Learning Trainingcited by 3
- 2022Carbon-Neutralized Task Scheduling for Green Computing Networkscited by 2
Built on by
Citing papers per year: 2025: 1; 2026: 1.
2 papers.