Green and sustainable AI research: an integrated thematic and topic modeling analysis

IF 8.6 2区计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS

Journal of Big Data Pub Date : 2024-04-22 DOI:10.1186/s40537-024-00920-x

Raghu Raman, Debidutta Pattnaik, Hiran H. Lathabai, Chandan Kumar, Kannan Govindan, Prema Nedungadi

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Abstract

This investigation delves into Green AI and Sustainable AI literature through a dual-analytical approach, combining thematic analysis with BERTopic modeling to reveal both broad thematic clusters and nuanced emerging topics. It identifies three major thematic clusters: (1) Responsible AI for Sustainable Development, focusing on integrating sustainability and ethics within AI technologies; (2) Advancements in Green AI for Energy Optimization, centering on energy efficiency; and (3) Big Data-Driven Computational Advances, emphasizing AI’s influence on socio-economic and environmental aspects. Concurrently, BERTopic modeling uncovers five emerging topics: Ethical Eco-Intelligence, Sustainable Neural Computing, Ethical Healthcare Intelligence, AI Learning Quest, and Cognitive AI Innovation, indicating a trend toward embedding ethical and sustainability considerations into AI research. The study reveals novel intersections between Sustainable and Ethical AI and Green Computing, indicating significant research trends and identifying Ethical Healthcare Intelligence and AI Learning Quest as evolving areas within AI’s socio-economic and societal impacts. The study advocates for a unified approach to innovation in AI, promoting environmental sustainability and ethical integrity to foster responsible AI development. This aligns with the Sustainable Development Goals, emphasizing the need for ecological balance, societal welfare, and responsible innovation. This refined focus underscores the critical need for integrating ethical and environmental considerations into the AI development lifecycle, offering insights for future research directions and policy interventions.

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绿色和可持续人工智能研究：专题和主题建模综合分析

本调查通过双重分析方法深入研究绿色人工智能和可持续人工智能文献，将主题分析与 BERTopic 模型相结合，以揭示广泛的主题集群和细微的新兴主题。研究确定了三大主题集群：(1) 负责任的人工智能促进可持续发展，侧重于将可持续性和伦理融入人工智能技术；(2) 绿色人工智能在能源优化方面的进展，以能源效率为中心；(3) 大数据驱动的计算进展，强调人工智能对社会经济和环境方面的影响。同时，BERTopic 模型还揭示了五个新兴主题：伦理生态智能、可持续神经计算、伦理医疗智能、人工智能学习探索和认知人工智能创新，表明了将伦理和可持续发展因素纳入人工智能研究的趋势。研究揭示了可持续和伦理人工智能与绿色计算之间的新交叉点，指出了重要的研究趋势，并将伦理医疗智能和人工智能学习探索确定为人工智能对社会经济和社会影响中不断发展的领域。该研究倡导采用统一的方法进行人工智能创新，促进环境可持续性和道德诚信，以推动负责任的人工智能发展。这与可持续发展目标相一致，强调了生态平衡、社会福利和负责任创新的必要性。这一细化的重点强调了将伦理和环境因素纳入人工智能发展生命周期的迫切需要，为未来的研究方向和政策干预提供了启示。

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来源期刊

Journal of Big Data Computer Science-Information Systems

CiteScore

17.80

自引率

3.70%

发文量

105

审稿时长

13 weeks

期刊介绍： The Journal of Big Data publishes high-quality, scholarly research papers, methodologies, and case studies covering a broad spectrum of topics, from big data analytics to data-intensive computing and all applications of big data research. It addresses challenges facing big data today and in the future, including data capture and storage, search, sharing, analytics, technologies, visualization, architectures, data mining, machine learning, cloud computing, distributed systems, and scalable storage. The journal serves as a seminal source of innovative material for academic researchers and practitioners alike.