Sustainable AI: Environmental Implications, Challenges and Opportunities

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1. Very close Same question Not relevant

   Efficiency is Not Enough: A Critical Perspective of Environmentally Sustainable AI

   Dustin Wright, Christian Igel, Gabrielle Samuel et al. · 2023 · Machine Learning · AI summary available

   “Efficiency is Not Enough” examines environmental sustainability of machine learning, including its energy use and broader environmental impacts, as does this paper.

   Abstract

   Artificial Intelligence (AI) is currently spearheaded by machine learning (ML) methods such as deep learning (DL) which have accelerated progress on many tasks thought to be out of reach of AI. These ML methods can often be compute hungry, energy intensive, and result in significant carbon emissions, a known driver of anthropogenic climate change. Additionally, the platforms on which ML systems run are associated with environmental impacts including and beyond carbon emissions. The solution lionized by both industry and the ML community to improve the environmental sustainability of ML is to increase the efficiency with which ML systems operate in terms of both compute and energy consumption…

2. Close Broader context Not relevant

   AI-enabled Efficient and Safe Food Supply Chain

   Ilianna Kollia, Jack Stevenson, Stefanos Kollias · 2021 · Machine Learning · Not summarized yet

   “AI-enabled Efficient and Safe Food Supply Chain” reviews AI applications in food production and energy management rather than the environmental footprint of AI computing.

   Abstract

   This paper provides a review of an emerging field in the food processing sector, referring to efficient and safe food supply chains, from farm to fork, as enabled by Artificial Intelligence (AI). Recent advances in machine and deep learning are used for effective food production, energy management and food labeling. Appropriate deep neural architectures are adopted and used for this purpose, including Fully Convolutional Networks, Long Short-Term Memories and Recurrent Neural Networks, Auto-Encoders and Attention mechanisms, Latent Variable extraction and clustering, as well as Domain Adaptation. Three experimental studies are presented, illustrating the ability of these AI methodologies to …

3. Related Another approach to the problem Not relevant

   Measuring the Energy Consumption and Efficiency of Deep Neural Networks: An Empirical Analysis and Design Recommendations

   Charles Edison Tripp, Jordan Perr-Sauer, Jamil Gafur et al. · 2024 · Machine Learning · AI summary available

   “Measuring the Energy Consumption and Efficiency of Deep Neural Networks” measures training energy across neural network configurations, complementing this paper’s lifecycle analysis of AI’s carbon footprint.

   Abstract

   Addressing the so-called ``Red-AI'' trend of rising energy consumption by large-scale neural networks, this study investigates the actual energy consumption, as measured by node-level watt-meters, of training various fully connected neural network architectures. We introduce the BUTTER-E dataset, an augmentation to the BUTTER Empirical Deep Learning dataset, containing energy consumption and performance data from 63,527 individual experimental runs spanning 30,582 distinct configurations: 13 datasets, 20 sizes (number of trainable parameters), 8 network ``shapes'', and 14 depths on both CPU and GPU hardware collected using node-level watt-meters. This dataset reveals the complex relationship…

4. Related Broader context Not relevant

   AI/ML Algorithms and Applications in VLSI Design and Technology

   Deepthi Amuru, Harsha V. Vudumula, Pavan K. Cherupally et al. · 2022 · Machine Learning · AI summary available

   “AI/ML Algorithms and Applications in VLSI Design and Technology” focuses on using AI to improve chip design and manufacturing, not on reducing AI’s environmental footprint.

   Abstract

   An evident challenge ahead for the integrated circuit (IC) industry in the nanometer regime is the investigation and development of methods that can reduce the design complexity ensuing from growing process variations and curtail the turnaround time of chip manufacturing. Conventional methodologies employed for such tasks are largely manual; thus, time-consuming and resource-intensive. In contrast, the unique learning strategies of artificial intelligence (AI) provide numerous exciting automated approaches for handling complex and data-intensive tasks in very-large-scale integration (VLSI) design and testing. Employing AI and machine learning (ML) algorithms in VLSI design and manufacturing …

5. Related Broader context Not relevant

   Interpretable deep-learning models to help achieve the Sustainable Development Goals

   Ricardo Vinuesa, Beril Sirmacek · 2021 · Machine Learning · AI summary available

   “Interpretable deep-learning models to help achieve the Sustainable Development Goals” concerns interpretable AI for sustainable development, rather than the environmental impact of AI computing.

   Abstract

   We discuss our insights into interpretable artificial-intelligence (AI) models, and how they are essential in the context of developing ethical AI systems, as well as data-driven solutions compliant with the Sustainable Development Goals (SDGs). We highlight the potential of extracting truly-interpretable models from deep-learning methods, for instance via symbolic models obtained through inductive biases, to ensure a sustainable development of AI.

6. Related Broader context Not relevant

   A Survey on Bias and Fairness in Machine Learning

   Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena et al. · 2019 · Machine Learning · AI summary available

   “A Survey on Bias and Fairness in Machine Learning” reviews discriminatory outcomes in AI, a different sustainability concern from this paper’s focus on computing emissions.

   Abstract

   With the widespread use of AI systems and applications in our everyday lives, it is important to take fairness issues into consideration while designing and engineering these types of systems. Such systems can be used in many sensitive environments to make important and life-changing decisions; thus, it is crucial to ensure that the decisions do not reflect discriminatory behavior toward certain groups or populations. We have recently seen work in machine learning, natural language processing, and deep learning that addresses such challenges in different subdomains. With the commercialization of these systems, researchers are becoming aware of the biases that these applications can contain a…

7. Related Broader context Not relevant

   Personalizing Sustainable Agriculture with Causal Machine Learning

   Georgios Giannarakis, Vasileios Sitokonstantinou, Roxanne Suzette Lorilla et al. · 2022 · Machine Learning · AI summary available

   “Personalizing Sustainable Agriculture with Causal Machine Learning” applies machine learning to environmental impacts of agricultural practices, rather than impacts from AI hardware and computing.

   Abstract

   To fight climate change and accommodate the increasing population, global crop production has to be strengthened. To achieve the "sustainable intensification" of agriculture, transforming it from carbon emitter to carbon sink is a priority, and understanding the environmental impact of agricultural management practices is a fundamental prerequisite to that. At the same time, the global agricultural landscape is deeply heterogeneous, with differences in climate, soil, and land use inducing variations in how agricultural systems respond to farmer actions. The "personalization" of sustainable agriculture with the provision of locally adapted management advice is thus a necessary condition for t…

8. Related Broader context Not relevant

   Data-centric Artificial Intelligence: A Survey

   Daochen Zha, Zaid Pervaiz Bhat, Kwei-Herng Lai et al. · 2023 · Machine Learning · AI summary available

   “Data-centric Artificial Intelligence: A Survey” reviews data quality and data management for AI, while this paper examines environmental impacts across AI data, algorithms, and hardware.

   Abstract

   Artificial Intelligence (AI) is making a profound impact in almost every domain. A vital enabler of its great success is the availability of abundant and high-quality data for building machine learning models. Recently, the role of data in AI has been significantly magnified, giving rise to the emerging concept of data-centric AI. The attention of researchers and practitioners has gradually shifted from advancing model design to enhancing the quality and quantity of the data. In this survey, we discuss the necessity of data-centric AI, followed by a holistic view of three general data-centric goals (training data development, inference data development, and data maintenance) and the represen…