Greening AI-enabled Systems with Software Engineering: A Research Agenda for Environmentally Sustainable AI Practices

2025-06-02

AI Summary

The surging energy consumption of AI systems poses significant environmental challenges, necessitating sustainable solutions grounded in software engineering principles. This paper presents the first systematic integration of software engineering and Green AI, proposing a six-dimensional research agenda for AI’s environmental sustainability: standardized energy assessment, green benchmarking, sustainable architecture design, runtime adaptive optimization, empirical research methodologies, and pedagogical practices. Leveraging software engineering methodologies, energy-aware modeling, and empirical frameworks, we establish reusable technical pathways and an open problem catalog. Our contributions include actionable engineering practice guidelines, interdisciplinary priority rankings, and a collaborative industry–academia–research mechanism for carbon reduction. This work bridges a critical gap in the literature by establishing software engineering as a foundational discipline for advancing Green AI.

Technology Category

• Machine Learning: Efficient ML / Green AI
• Philosophy and Ethics of AI: Philosophical Foundations of AI
• Application Domains: Software Engineering

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Sustainability and carbon-aware systems for Web, mobile, and WoT
• Responsible Web: Sustainability and climate impact of web technologies
• Security and Privacy: Security and privacy of machine learning and AI applications

Abstract

The environmental impact of Artificial Intelligence (AI)-enabled systems is increasing rapidly, and software engineering plays a critical role in developing sustainable solutions. The"Greening AI with Software Engineering"CECAM-Lorentz workshop (no. 1358, 2025) funded by the Centre Europ'een de Calcul Atomique et Mol'eculaire and the Lorentz Center, provided an interdisciplinary forum for 29 participants, from practitioners to academics, to share knowledge, ideas, practices, and current results dedicated to advancing green software and AI research. The workshop was held February 3-7, 2025, in Lausanne, Switzerland. Through keynotes, flash talks, and collaborative discussions, participants identified and prioritized key challenges for the field. These included energy assessment and standardization, benchmarking practices, sustainability-aware architectures, runtime adaptation, empirical methodologies, and education. This report presents a research agenda emerging from the workshop, outlining open research directions and practical recommendations to guide the development of environmentally sustainable AI-enabled systems rooted in software engineering principles.

Problem

• Addressing environmental impact of AI systems through software engineering
• Developing sustainable solutions for green AI practices
• Identifying key challenges in energy assessment and standardization for AI

Innovation

• Energy assessment and standardization for AI
• Sustainability-aware architectures in software
• Runtime adaptation for green AI