Data-Centric Green Artificial Intelligence: A Survey

Salehi, Shirin; Schmeink, Anke

doi:10.1109/tai.2023.3315272

Cited by 9 publications

(3 citation statements)

References 71 publications

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“…Although there is a growing emphasis on Green AI against Red AI, the up-to-date standards mainly focus on the major complex ethical, environmental, and technical challenges that AI presents. Salehi et al [22] discuss the importance of data in relationship with AI and sustainability, concluding the need for data benchmarks rather than model benchmarks when working on data-centric AI. The authors provide 36 benchmarks, considering different tasks, goals, domain of application and state of their retrieved data.…”

Section: Structural Matricesmentioning

confidence: 99%

Toward Green AI: A Methodological Survey of the Scientific Literature

Barbierato,

Gatti

2024

IEEE Access

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The pervasive deployment of Deep Learning models has recently prompted apprehensions regarding their ecological footprint, owing to the exorbitant levels of energy consumption necessitated by the training and inference processes. The term "Red AI" is employed to denote artificial intelligence (AI) models that undergo training using resource-intensive methodologies on very large datasets. This practice can engender substantial energy usage and emissions of carbon, thereby opposing "Green AI." The latter concept alludes to AI models designed for similar efficiency and reduced environmental impact. This objective is realized through the utilization of smaller datasets, less computationally intensive training techniques, or sustainable energy resources. While Red AI prioritizes accuracy and performance, Green AI emphasizes efficiency and sustainability. Given that both paradigms exhibit advantages and limitations, the debates around the topics have burgeoned in the scientific arena, delving into novel algorithms, hardware innovations, and improved data utilization techniques aimed at mitigating the ecological consequences of intricate applications such as GPT and BERT. Nevertheless, due to the relative novelty of this debate, not much effort has been dedicated yet to contextualizing the essence of Red AI and the prospects of Green AI in a coherent framework. Within this context, the present work contributes by meticulously delineating both domains through a multifaceted analysis of their causes and ramifications, described from the points of computer architectures, data structures, and algorithms. Additionally, the study reviews notable instances of study cases based on complex Red AI models. The primary contribution of this article encompasses a comprehensive survey of Red and Green AI, stemming from a selection of the literature performed by the authors, subsequently organized into distinct clusters. These clusters encompass i) articles that qualitatively or quantitatively address the issue of Red AI, identifying Green AI as a plausible remedy, ii) articles offering insights into the environmental impact associated with the deployment of extensive Deep Learning models, and iii) articles introducing the techniques underpinning Green AI, aiming at mitigating the cost of Red AI. The outcome emerging from the analysis performed by this work consists of a compromise between sustainability in contrast to the performance of AI tools. Unless the complex training and inference procedures of software models mitigate their environmental impact, it will be necessary to decrease the level of accuracy of production systems, inevitably conflicting with the objective of the major AI vendors. The outcomes of this work would be beneficial to scholars pursuing intricate Deep Learning architectures in scientific research, as well as AI enterprises struggling with the protracted training demands of commercial products within the realms of Computer Vision and Natural Language Processing.INDEX TERMS green ai, red ai, survey, environmenta...

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Section: Structural Matricesmentioning

confidence: 99%

Toward Green AI: A Methodological Survey of the Scientific Literature

Barbierato,

Gatti

2024

IEEE Access

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“…Solutions like need-based model development, flagging vulnerabilities in data, getting data from all possible cases, and data condensation, can help resolve sustainability problems. Recently, some frameworks such as core-set selection, active learning, knowledge transfer, curriculum learning, data augmentation, depth-wise separable convolution, parameter pruning, weight sharing, etc., have also been suggested to resolve the sustainability prob-lems of AI/ML models [103,104]. Improved affordance can overcome the imbalance of technology adoption between small businesses and big businesses.…”

Section: Insight Into Suggested Solutions and Their Feasibility In Ad...mentioning

confidence: 99%

A Data-Centric AI Paradigm for Socio-Industrial and Global Challenges

Majeed,

Hwang

2024

Electronics

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Due to huge investments by both the public and private sectors, artificial intelligence (AI) has made tremendous progress in solving multiple real-world problems such as disease diagnosis, chatbot misbehavior, and crime control. However, the large-scale development and widespread adoption of AI have been hindered by the model-centric mindset that only focuses on improving the code/architecture of AI models (e.g., tweaking the network architecture, shrinking model size, tuning hyper-parameters, etc.). Generally, AI encompasses a model (or code) that solves a given problem by extracting salient features from underlying data. However, when the AI model yields a low performance, developers iteratively improve the code/algorithm without paying due attention to other aspects such as data. This model-centric AI (MC-AI) approach is limited to only those few businesses/applications (language models, text analysis, etc.) where big data readily exists, and it cannot offer a feasible solution when good data are not available. However, in many real-world cases, giant datasets either do not exist or cannot be curated. Therefore, the AI community is searching for appropriate solutions to compensate for the lack of giant datasets without compromising model performance. In this context, we need a data-centric AI (DC-AI) approach in order to solve the problems faced by the conventional MC-AI approach, and to enhance the applicability of AI technology to domains where data are limited. From this perspective, we analyze and compare MC-AI and DC-AI, and highlight their working mechanisms. Then, we describe the crucial problems (social, performance, drift, affordance, etc.) of the conventional MC-AI approach, and identify opportunities to solve those crucial problems with DC-AI. We also provide details concerning the development of the DC-AI approach, and discuss many techniques that are vital in bringing DC-AI from theory to practice. Finally, we highlight enabling technologies that can contribute to realizing DC-AI, and discuss various noteworthy use cases where DC-AI is more suitable than MC-AI. Through this analysis, we intend to open up a new direction in AI technology to solve global problems (e.g., climate change, supply chain disruption) that are threatening human well-being around the globe.

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“…Developing an AI model for AIED involves training deep learning models on vast amounts of data. This consumes a significant amount of energy during both training and validation and these computations have a large carbon footprint [54]. Another issue is that AIED and LA require extensive datasets, usually stored on servers in data centres.…”

Section: Energy Consumption and Disposal Issues Of Edtechmentioning

confidence: 99%

Perspective Chapter: Educational Technology under Scrutiny in Higher Education – A Framework for Balancing Environmental, Economic and Social Aspects in a Blended Design

Versteijlen,

Janssen Groesbeek

2024

Sustainable Development

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Following COVID-19, higher education (HE) has recognised the importance of educational technology (EdTech). With its growing influence on educational design, awareness of its role and impact on the sustainability transition in HE from an economic, environmental and social perspective is needed. Taking a holistic view, this chapter shows the opportunities and drawbacks of using EdTech in HE aiming at developing suggestions for responsible application. Economically, there are concerns because the earnings models of for-profit EdTech companies are focused on obtaining user data and benefit from a lack of transparency in data use, privacy and intellectual property. Environmentally, the use of EdTech can reduce the carbon footprint of HE institutions by opening a virtual space where students can learn and faculty can attend international conferences without commuting or travelling (by plane). However, device disposal and the energy consumption of hardware and data storage must be considered. Socially, using EdTech can foster the development of sustainability competencies if thoughtfully designed by applying pedagogical design principles for sustainability-oriented blended learning. Higher education can take control of a balanced use of EdTech in educational practice by focusing on ethical and human values and adopting a whole-institution approach to sustainability as included in the proposed framework.

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Data-Centric Green Artificial Intelligence: A Survey

Cited by 9 publications

References 71 publications

Toward Green AI: A Methodological Survey of the Scientific Literature

Toward Green AI: A Methodological Survey of the Scientific Literature

A Data-Centric AI Paradigm for Socio-Industrial and Global Challenges

Perspective Chapter: Educational Technology under Scrutiny in Higher Education – A Framework for Balancing Environmental, Economic and Social Aspects in a Blended Design

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