Innovating for Tomorrow: The Convergence of SE and Green AI
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Abstract (arXiv)
The latest advancements in machine learning, specifically in foundation models, are revolutionizing the frontiers of existing software engineering (SE) processes. This is a bi-directional phenomona, where 1) software systems are now challenged to provide AI-enabled features to their users, and 2) AI is used to automate tasks within the software development lifecycle. In an era where sustainability is a pressing societal concern, our community needs to adopt a long-term plan enabling a conscious transformation that aligns with environmental sustainability values. In this paper, we reflect on the impact of adopting environmentally friendly practices to create AI-enabled software systems and make considerations on the environmental impact of using foundation models for software development.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 17 of its 54 references that are in the map. First posted on arXiv 26 Jun 2024.
Builds on
17 of 54 references.
References · 17
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5 more
- 2021Which Design Decisions in AI-enabled Mobile Applications Contribute to Greener AI?cited by 6
- 2023Estimating Deep Learning energy consumption based on model architecture and training environmentcited by 4
- 2024Data vs. Model Machine Learning Fairness Testing: An Empirical Studycited by 4
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Built on by
Citing papers per year: 2024: 2; 2025: 0; 2026: 1.
3 papers.