Sustainable AI: Environmental Implications, Challenges and Opportunities

Wu, Carole-Jean; Raghavendra, Ramya; Gupta, Udit; Acun, Bilge; Ardalani, Newsha; Maeng, Kiwan; Chang, Gloria; Behram, Fiona Aga; Huang, James; Bai, Charles; Gschwind, Michael; Gupta, Anurag; Ott, Myle; Мельников, А С; Candido, Salvatore; Brooks, David J.; Chauhan, G. S.; Lee, Benjamin; Lee, Hsien-Hsin S.; Akyildiz, Bugra; Balandat, Maximilian; Spisak, Joe; Jain, Ravi; Rabbat, Mike; Hazelwood, Kim

doi:10.48550/arxiv.2111.00364

Cited by 22 publications

(19 citation statements)

References 48 publications

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“…AI literature mostly addresses a small part of direct impacts and neglects production and end of life, thus not following recommendations such as [16]. In [12,17], the authors point out the methodological gaps of the previous studies, focusing on the use phase. In particular, manufacturing would account for about 75% of the total emissions of Apple or of an iPhone 5, just to give examples of various scales.…”

Section: Carbon Footprint Of Aimentioning

confidence: 99%

“…At the same time, AI's popularity is increasing, and AI is often presented as a solution to environmental problems with AI for Green proposals [9][10][11]. The negative environmental impacts can be briefly evoked-and in particular, rebound effects [9,12] where unitary efficiency gains can lead to global GHG increase-but no quantification of all AI's environmental costs is proposed to close the loop between AI for Green and Green AI. That is why it is even more important to be able to assess the actual impacts, taking into account both positive and negative effects.…”

Section: Introductionmentioning

confidence: 99%

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Unraveling the Hidden Environmental Impacts of AI Solutions for Environment Life Cycle Assessment of AI Solutions

et al. 2022

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In the past ten years, artificial intelligence has encountered such dramatic progress that it is now seen as a tool of choice to solve environmental issues and, in the first place, greenhouse gas emissions (GHG). At the same time, the deep learning community began to realize that training models with more and more parameters require a lot of energy and, as a consequence, GHG emissions. To our knowledge, questioning the complete net environmental impacts of AI solutions for the environment (AI for Green) and not only GHG, has never been addressed directly. In this article, we propose to study the possible negative impacts of AI for Green. First, we review the different types of AI impacts; then, we present the different methodologies used to assess those impacts and show how to apply life cycle assessment to AI services. Finally, we discuss how to assess the environmental usefulness of a general AI service and point out the limitations of existing work in AI for Green.

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Section: Carbon Footprint Of Aimentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Unraveling the Hidden Environmental Impacts of AI Solutions for Environment Life Cycle Assessment of AI Solutions

et al. 2022

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“…Although DL-based approaches show exceptional performance in various fields, they are highly dependent on heavy computation and arithmetic operations, resulting in high power consumption. Higher energy cost means increased amounts of carbon dioxide equivalent (CO 2 e) emissions, constituting the main reason for global warming and climate change [219]. Recent studies have been devoted to investigating the impact of ML on Earth's climate [220]- [222], steering the focus to the environmental effects of training large-scale ML models connected to network grids powered using fossil fuels.…”

Section: H Fl Carbon Footprintmentioning

confidence: 99%

Edge-Native Intelligence for 6G Communications Driven by Federated Learning: A Survey of Trends and Challenges

Al-Quraan¹,

Mohjazi²,

Bariah³

et al. 2021

Preprint

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The unprecedented surge of data volume in wireless networks empowered with artificial intelligence (AI) opens up new horizons for providing ubiquitous data-driven intelligent services. Traditional cloud-centric machine learning (ML)-based services are implemented by collecting datasets and training models centrally. However, this conventional training technique encompasses two challenges: (i) high communication and energy cost due to increased data communication, (ii) threatened data privacy by allowing untrusted parties to utilise this information. Recently, in light of these limitations, a new emerging technique, coined as federated learning (FL), arose to bring ML to the edge of wireless networks. FL can extract the benefits of data silos by training a global model in a distributed manner, orchestrated by the FL server. FL exploits both decentralised datasets and computing resources of participating clients to develop a generalised ML model without compromising data privacy. In this article, we introduce a comprehensive survey of the fundamentals and enabling technologies of FL. Moreover, an extensive study is presented detailing various applications of FL in wireless networks and highlighting their challenges and limitations. The efficacy of FL is further explored with emerging prospective beyond fifth generation (B5G) and sixth generation (6G) communication systems. The purpose of this survey is to provide an overview of the state-of-the-art of FL applications in key wireless technologies that will serve as a foundation to establish a firm understanding of the topic. Lastly, we offer a road forward for future research directions.

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“…(3) reasons like easy-sharing, fast-experimentation, mitigating the significant environmental footprint of training resource-hungry machine learning models [11,37,43,50]. In this paper, we are interested in finding a sub-sample of a dataset which has minimal effects on model utility evaluation i.e.…”

Section: Introductionmentioning

confidence: 99%

On Sampling Collaborative Filtering Datasets

Sachdeva

McAuley

2022

Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining

Self Cite

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We study the practical consequences of dataset sampling strategies on the ranking performance of recommendation algorithms. Recommender systems are generally trained and evaluated on samples of larger datasets. Samples are often taken in a naïve or ad-hoc fashion: e.g. by sampling a dataset randomly or by selecting users or items with many interactions. As we demonstrate, commonly-used data sampling schemes can have significant consequences on algorithm performance. Following this observation, this paper makes three main contributions: (1) characterizing the effect of sampling on algorithm performance, in terms of algorithm and dataset characteristics (e.g. sparsity characteristics, sequential dynamics, etc.);(2) designing SVP-CF, which is a data-specific sampling strategy, that aims to preserve the relative performance of models after sampling, and is especially suited to long-tailed interaction data; and (3) developing an oracle, Data-genie, which can suggest the sampling scheme that is most likely to preserve model performance for a given dataset. The main benefit of Data-genie is that it will allow recommender system practitioners to quickly prototype and compare various approaches, while remaining confident that algorithm performance will be preserved, once the algorithm is retrained and deployed on the complete data. Detailed experiments show that using Data-genie, we can discard upto 5× more data than any sampling strategy with the same level of performance.

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Sustainable AI: Environmental Implications, Challenges and Opportunities

Cited by 22 publications

References 48 publications

Unraveling the Hidden Environmental Impacts of AI Solutions for Environment Life Cycle Assessment of AI Solutions

Unraveling the Hidden Environmental Impacts of AI Solutions for Environment Life Cycle Assessment of AI Solutions

Edge-Native Intelligence for 6G Communications Driven by Federated Learning: A Survey of Trends and Challenges

On Sampling Collaborative Filtering Datasets

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