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Green AI, a reality or Green Washing?

 


In this blog post, I will summarise the main findings of a recent paper titled "A Systematic Review of Green AI" by Verdecchia et al.. The paper provides a comprehensive overview of the research field of Green AI, which aims to reduce the carbon footprint of AI models and systems. The paper analyzes 98 primary studies on Green AI published between 2016 and 2021, and identifies different patterns and trends in the literature.

A definition of Green AI. From the results regarding how the term “Green AI” is used in the literature a clear picture emerges. Most Green AI studies consider Green AI as exclusively related to energy efficiency. Only fewer studies examine the influence of AI on greenhouse gas emissions (𝐢𝑂2), and an even minor fraction examines the holistic impact that AI has on the natural environment.

The paper categorises the Green AI studies into four main types: position papers, observational studies, solution papers, and tool papers. Position papers propose new definitions, frameworks, or challenges for Green AI. Observational studies measure or estimate the energy consumption or carbon emissions of AI models or systems. Solution papers propose new methods, techniques, or algorithms to improve the environmental sustainability of AI models or systems. Tool papers present new tools or frameworks to support Green AI research or practice.

The paper also analyses the Green AI studies according to different dimensions, such as the target phase (training, inference, or deployment), the target algorithm (algorithm-agnostic, neural networks, or others), the target data type (image, text, speech, or others), the research strategy (laboratory experiments, case studies, surveys, or others), the reported energy savings (percentage of energy reduction compared to a baseline), the industrial involvement (whether the study involves industrial partners or targets industrial readers), and the tool provisioning (whether the study provides a tool or framework for Green AI).

The paper reports several interesting findings from the analysis. For example:

  • The topic of Green AI experienced a considerable growth from 2020 onward, with more than half of the primary studies published in 2020 and 2021.
  • Most studies focus on monitoring AI model footprint, tuning hyperparameters to improve model sustainability, or benchmarking models.
  • 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, followed by case studies and surveys.
  • 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, with only 10% of the primary studies providing a tool or framework for Green AI.

The paper concludes that the Green AI research field has reached a considerable level of maturity, and suggests that future research should adopt other Green AI research strategies, such as conducting more case studies and surveys, and porting the promising academic results to industrial practice.

I hope this blog post has given you a brief overview of the paper "A Systematic Review of Green AI" by Verdecchia et al.. If you are interested in learning more about Green AI research and practice, I recommend you to read the full paper and check out some of the references  it cites.

: Verdecchia et al., "A Systematic Review of Green AI", arXiv:2301.11047 [cs.AI], 2023

: Strubell et al., "Energy and Policy Considerations for Deep Learning in NLP", ACL 2019

: Schwartz et al., "Green AI", Communications of ACM 2020

What seems clear from the paper is that given a lot more focus the companies developing AI could save a considerable amount of resources in both the development and deployment stages. One of the concluding remarks of the paper it is noted:' The potential of Green AI cannot be disregarded: the majority of publications show significant energy savings, up to 115%, at little or no cost in accuracy.'

However, until there is a consistent use of terminology and understanding of the term Green AI, that goes beyond the associated energy consumption, it is not a term of much utility. 


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