A roadmap and comprehensive record for development of sustainable AI in both social wise and environmental wise.
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2."Survey on AI Sustainability: Emerging Trends on Learning Algorithms and Research Challenges." IEEE Computational Intelligence Magazine 18.2 (2023): 60-77.
3."Sustainable ai: Environmental implications, challenges and opportunities."Proceedings of Machine Learning and Systems 4 (2022): 795-813.
4."Carbon emissions and large neural network training" arXiv preprint arXiv:2104.10350 (2021)./System/Calculator
5."On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?🦜." Proceedings of the 2021 ACM conference on fairness, accountability, and transparency. 2021./NLP
6."Towards the systematic reporting of the energy and carbon footprints of machine learning." The Journal of Machine Learning Research 21.1 (2020): 10039-10081./RL
7."Energy and policy considerations for deep learning in NLP."arXiv preprint arXiv:1906.02243 (2019)./NLP/Calculator
8."Evaluating the carbon footprint of NLP methods: a survey and analysis of existing tools." Proceedings of the Second Workshop on Simple and Efficient Natural Language Processing. 2021./NLP/Calculator
9."Quantifying the carbon emissions of machine learning." arXiv preprint arXiv:1910.09700 (2019)./Calculator