mlsanityData to 14 Oct 2026

A Survey on AI Sustainability: Emerging Trends on Learning Algorithms and Research Challenges

arXiv 2205.03824 ↗First posted 8 May 2022cs.AIcited by 7

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Abstract (arXiv)

Artificial Intelligence (AI) is a fast-growing research and development (R&D) discipline which is attracting increasing attention because of its promises to bring vast benefits for consumers and businesses, with considerable benefits promised in productivity growth and innovation. To date it has reported significant accomplishments in many areas that have been deemed as challenging for machines, ranging from computer vision, natural language processing, audio analysis to smart sensing and many others. The technical trend in realizing the successes has been towards increasing complex and large size AI models so as to solve more complex problems at superior performance and robustness. This rapid progress, however, has taken place at the expense of substantial environmental costs and resources. Besides, debates on the societal impacts of AI, such as fairness, safety and privacy, have continued to grow in intensity. These issues have presented major concerns pertaining to the sustainable development of AI. In this work, we review major trends in machine learning approaches that can address the sustainability problem of AI. Specifically, we examine emerging AI methodologies and algorithms for addressing the sustainability issue of AI in two major aspects, i.e., environmental sustainability and social sustainability of AI. We will also highlight the major limitations of existing studies and propose potential research challenges and directions for the development of next generation of sustainable AI techniques. We believe that this technical review can help to promote a sustainable development of AI R&D activities for the research community.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 140 of its 207 references that are in the map. First posted on arXiv 8 May 2022.

Builds on

140 of 207 references.

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50 most cited of 140 All 140 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 2; 2025: 1; 2026: 3.

7 papers.

  1. 2024Development and Testing of Retrieval Augmented Generation in Large Language Models -- A Case Study Reportcited by 13
  2. 2024oRetrieval Augmented Generation for 10 Large Language Models and its Generalizability in Assessing Medical Fitnesscited by 1
  3. 2025The Unbearable Lightness of Prompting: A Critical Reflection on the Environmental Impact of genAI use in Design Educationcited by 1
  4. 2026Artificial Intelligence in Open Source Software Engineering: A Foundation for Sustainabilitycited by 1
  5. 2023The Energy Prediction Smart-Meter Dataset: Analysis of Previous Competitions and Beyondcited by 0
  6. 2026Green Physics-Informed Machine Learning Models For Structural Health Monitoringcited by 0
  7. 2026A fairness-aware extension of Stochastic Multicriteria Acceptability Analysis for rankingcited by 0