Sustainability challenges of Artificial Intelligence and Policy Implications

Rohde, Friederike; Gossen, Maike; Wagner, Jorge R.; Santarius, Tilman

doi:10.14512/oewo360136

Cited by 16 publications

(11 citation statements)

References 5 publications

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“…Data centers have become a preferred object of study when investigating digital infrastructures, not least because they are the site of intersection for a range of pressing issues such as data mining, large-scale surveillance, geopolitics, and data sovereignty. Data centers have provoked discussions about the corporate use of public services (Hogan 2015;Brodie 2020) and stimulated reflections on land use and physical space (Johnson 2019;Vonderau 2019;Mayer 2020), on power sources and cooling systems (Hogan 2015;Velkova 2016) and on the energy demand of machine learning (Rohde et al 2021;Tarnoff 2020).…”

Section: The Infrastructure Of Video Streamingmentioning

confidence: 99%

4. Streaming against the Environment: Digital Infrastructures, Video Compression, and the Environmental Footprint of Video Streaming

Jancovic¹,

Keilbach²

2023

Situating Data

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Section: The Infrastructure Of Video Streamingmentioning

confidence: 99%

4. Streaming against the Environment: Digital Infrastructures, Video Compression, and the Environmental Footprint of Video Streaming

Jancovic¹,

Keilbach²

2023

Situating Data

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“…Perucica and Andjelkovic [81] reflect on the environmental policies implemented by the European Union, discussing whether they fit the AI era or new regulations are needed. Rhode et al [85] call out for the unclear dilemma between the impact of existing/upcoming AI technologies and the commitment to achieve the 1.5℃ climate change goal as expressed in the UNFCCC Paris Declaration.…”

Section: Green Ai Topicsmentioning

confidence: 99%

“…Interestingly, among the primary studies, few are intended also for policymakers, i.e., aim to sensibilize government stakeholders to consider issues related to Green AI. For example, in a paper by Rohde et al [85], how opportunities and risks for the environment, economy and society associated with AI can be governed are discussed.…”

Section: Intended Readersmentioning

confidence: 99%

A Systematic Review of Green AI

Verdecchia¹,

Sallou²,

Cruz³

2023

Preprint

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With the ever-growing adoption of AI-based systems, the carbon footprint of AI is no longer negligible. AI researchers and practitioners are therefore urged to hold themselves accountable for the carbon emissions of the AI models they design and use. This led in recent years to the appearance of researches tackling AI environmental sustainability, a field referred to as Green AI. Despite the rapid growth of interest in the topic, a comprehensive overview of Green AI research is to date still missing. To address this gap, in this paper, we present a systematic review of the Green AI literature. From the analysis of 98 primary studies, different patterns emerge. The topic experienced a considerable growth from 2020 onward. Most studies consider monitoring AI model footprint, tuning hyperparameters to improve model sustainability, or benchmarking models. A mix of position papers, observational studies, and solution papers are present. Most papers focus on the training phase, are algorithm-agnostic or study neural networks, and use image data. Laboratory experiments are the most common research strategy. Reported Green AI energy savings go up to 115%, with savings over 50% being rather common. Industrial parties are involved in Green AI studies, albeit most target academic readers. Green AI tool provisioning is scarce. As a conclusion, the Green AI research field results to have reached a considerable level of maturity. Therefore, from this review emerges that the time is suitable to adopt other Green AI research strategies, and port the numerous promising academic results to industrial practice.

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“…Data centers have become a preferred object of study when investigating digital infrastructures, not least because they are the site of intersection for a range of pressing issues such as data mining, large-scale surveillance, geopolitics, and data sovereignty. Data centers have provoked discussions about the corporate use of public services (Hogan 2015;Brodie 2020) and stimulated reflections on land use and physical space (Johnson 2019;Vonderau 2019;Mayer 2020), on power sources and cooling systems (Hogan 2015; Velkova 2016) and on the energy demand of machine learning (Rohde et al 2021;Tarnoff 2020).…”

Section: The Infrastructure Of Video Streamingmentioning

confidence: 99%

Situating Data

2023

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This collection examines the impact of data, datafication, and algorithmization on contemporary culture, and thereby also on the agenda of the broader field of cultural inquiry. Our perspective is double. We ask: Where and how do data and algorithms shape and transform culture? But also, where and how do they impact and transform scholarly practices in the study of culture? Situating data as cultural inquiry, thus, is not only an act of localizing data both in and as culture, but also an act of situating our perspective on, and knowledge about, this culture. With the selection of chapters in this collection, contributing to the diversification of cultural inquiry, we propose conceptual and methodological directions for exploring where, when, and how data and algorithms (re)shape cultural practices, create (in)justice, and produce knowledge.

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Sustainability challenges of Artificial Intelligence and Policy Implications

Cited by 16 publications

References 5 publications

4. Streaming against the Environment: Digital Infrastructures, Video Compression, and the Environmental Footprint of Video Streaming

4. Streaming against the Environment: Digital Infrastructures, Video Compression, and the Environmental Footprint of Video Streaming

A Systematic Review of Green AI

Situating Data

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