Data and System Perspectives of Sustainable Artificial Intelligence
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Abstract (arXiv)
Sustainable AI is a subfield of AI for concerning developing and using AI systems in ways of aiming to reduce environmental impact and achieve sustainability. Sustainable AI is increasingly important given that training of and inference with AI models such as large langrage models are consuming a large amount of computing power. In this article, we discuss current issues, opportunities and example solutions for addressing these issues, and future challenges to tackle, from the data and system perspectives, related to data acquisition, data processing, and AI model training and inference.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 7 of its 33 references that are in the map. First posted on arXiv 13 Jan 2025.
Builds on
7 of 33 references.
References · 7
- 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
- 2019Energy and Policy Considerations for Deep Learning in NLPcited by 1,242
- 2021A Survey of Data Augmentation Approaches for NLPcited by 408
- 2022Log-based Anomaly Detection with Deep Learning: How Far Are We?cited by 56
- 2022In Defense of Core-set: A Density-aware Core-set Selection for Active Learningcited by 26
- 2019AlphaClean: Automatic Generation of Data Cleaning Pipelinescited by 23
- 2022Synthetic Dataset Generation for Privacy-Preserving Machine Learningcited by 1
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Citing papers per year: 2025: 1; 2026: 1.
2 papers.