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https://ceur-ws.org/Vol-3934/preface.pdf
Preface to the Proceedings of Green-Aware AI 2024
Riccardo Cantini1 , Davide Mario Longo1,∗ and Dipanwita Thakur1
1
University of Calabria, Rende, Italy
1. Introduction
Artificial Intelligence (AI) is becoming increasingly integral to modern society, yet its environmental
impact and ethical implications remain critical concerns. The 1st Workshop on Green-Aware Artificial
Intelligence aims to address these challenges by bringing together scholars from various disciplines to
explore the intersection of Green AI—which focuses on energy-efficient and environmentally friendly
AI systems—and Sustainable AI, which promotes the development of AI technologies that align with
human-centered values and broader sustainability goals.
The importance of this workshop is underscored by recent advancements and challenges in AI
sustainability. As AI models grow larger and more resource-intensive, concerns about their energy
consumption and carbon emissions have intensified. At the same time, the need for low-power AI
solutions for edge computing and IoT devices has created new opportunities for research in energy-
efficient algorithms and hardware. Furthermore, ethics and responsibility in AI sustainability are
gaining traction, emphasizing the importance of aligning AI development with principles such as
fairness, transparency, and accountability. The primary objective of this workshop is to build a research
community focused on advancing Green and Sustainable AI and to foster interdisciplinary discussions,
enabling participants to exchange ideas, share research findings, and explore collaborative opportunities.
The workshop was co-located with the 23rd International Conference of the Italian Association for
Artificial Intelligence (AIxIA 2024), held from November 25–28, 2024, in Bolzano, Italy. This event
provided a broader academic context, enabling cross-disciplinary engagement between AI sustainability
researchers, ethicists, and industry practitioners. A key highlight of the event was an invited talk
by Prof. Kees van Berkel (TU Wien), titled AI Alignment and Normative Reasoning. His presentation
explored the ethical dimensions of AI sustainability, emphasizing how AI systems must be aligned
with human values, ethics, and laws. In particular, he highlighted the role of normative reasoning and
conflict resolution mechanisms in ensuring that AI operates within ethical and sustainable boundaries.
This workshop represents a crucial step in fostering a community committed to sustainable and
ethical AI development. By tackling the technical and ethical challenges of energy-efficient AI and
ensuring that technologies align with human values, it lays the foundation for responsible innovation.
It may also inspire future research and collaboration in advancing Green and Sustainable AI, fostering
the development of innovative solutions that balance technological progress with ethical, social, and
environmental responsibility.
2. Workshop Scope and Themes
The 1st Workshop on Green-Aware Artificial Intelligence focuses on the intersection of AI, sustainability,
and energy efficiency, bringing together researchers from diverse fields to explore new methods and
technologies that promote environmentally responsible AI development. The workshop addresses key
challenges in reducing AI’s carbon footprint, improving energy efficiency, and ensuring AI systems
1st Workshop on Green-Aware Artificial Intelligence, 23rd International Conference of the Italian Association for Artificial
Intelligence (AIxIA 2024), November 25–28, 2024, Bolzano, Italy
∗
Corresponding author.
Envelope-Open rcantini@dimes.unical.it (R. Cantini); davidemario.longo@dimes.unical.it (D. M. Longo);
dipanwita.thakur@dimes.unical.it (D. Thakur)
Orcid 0000-0003-3053-6132 (R. Cantini); 0000-0003-4018-4994 (D. M. Longo); 0000-0003-2895-1425 (D. Thakur)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
align with ethical and sustainable principles. It covered a broad range of topics, including but not
limited to:
• Energy-efficient AI Algorithms – Strategies to optimize machine learning models for reduced
energy consumption.
• Human-centered Green AI Design – Ensuring AI technologies align with human values and ethical
considerations.
• Ethical Considerations, Sustainability, and Privacy Preservation – Balancing AI advancements with
responsible governance.
• Reliability, Trustworthiness, and Interpretability in AI Applications – Ensuring robust and trustwor-
thy AI deployments across different sectors.
• Green Federated Learning and Edge AI – Methods to optimize AI for decentralized and edge-cloud
computing environments.
• Theoretical Analysis of Energy Efficiency in AI Applications – Exploring the mathematical principles
and computational frameworks underlying energy-efficient AI systems.
• Green AI Case Studies and Deployments – Real-world applications and lessons learned from
sustainable AI implementations.
• Sustainable AI Applications in Environmental and Social Sciences, Healthcare, Smart Cities, and
Energy Optimization – Leveraging AI for positive environmental and societal impact.
• Parallel and Distributed Algorithms for Energy-efficient AI – Advancing distributed AI computing
techniques to enhance energy efficiency and performance.
• Energy-aware Training Strategies for Scaling Up Language Models – Investigating the balance
between model size, accuracy, and energy efficiency to ensure sustainable scaling.
• Energy-aware Strategies to Support AI on Resource-constrained Devices – Developing AI for IoT
and low-power devices.
• Compression Techniques and Small Language Models – Exploring techniques such as pruning,
quantization, and distillation for efficient AI.
• Future Trends and Innovations in Green and Sustainable AI – Emerging research directions in AI
sustainability.
3. Contributions and Paper Selection
The 1st Workshop on Green-Aware Artificial Intelligence received a total of 8 paper submissions. Fol-
lowing a rigorous peer-review process, 7 papers were accepted, resulting in an 87.5% acceptance
rate, which included 4 regular papers and 3 short papers. These papers reflected a diverse range of
contributions within the field of Green and Sustainable AI, showcasing various innovative approaches
and solutions for sustainable AI practices. Among the explored topics and research areas, we mention
the environmental impact of AI algorithms, energy-efficient optimization techniques, and sustainability-
driven decision-making frameworks. Several works focused on improving energy consumption in
domains such as smart agriculture and sustainable building design, leveraging machine learning models
for enhanced efficiency. Others introduced novel approaches for anomaly detection with lightweight
feature extraction and meta-learning strategies, enabling more resource-efficient AI systems. Addition-
ally, research examined methods for green-aware temporal reasoning, efficient AI training paradigms,
and the identification of key factors that contribute to national sustainability advantages. These contri-
butions highlight the increasing intersection of AI and environmental consciousness, pushing forward
innovations that promote sustainable and responsible AI development.
Each submitted paper underwent a single-blind peer review process, where two independent review
were conducted for each paper. The workshop chairs made the final acceptance decision, based on
the feedback provided by the program committee members. Papers were evaluated based on standard
academic criteria, considering originality, technical quality, relevance to the field, potential impact on AI
sustainability, and overall clarity.
3.1. Program Committee
We would like to express our gratitude to the Program Committee members for their dedication in
reviewing the submissions and providing insightful feedback. Their expertise and thorough evaluations
were crucial in ensuring the quality and academic rigor of the selected papers. We acknowledge the
following PC members for their role in the review process:
• Ram Sarkar, Jadavpur University, West Bengal, India
• Debashis De, Maulana Abul Kalam Azad University of Technology, West Bengal, India
• Saroj Biswas, NIT Silchar, India
• Suparna Biswas, Maulana Abul Kalam Azad University of Technology, West Bengal, India
• Sandip Roy, Old Dominion University, USA
• Alessio Orsino, University of Calabria, Italy
• Paolo Lindia, University of Calabria, Italy
• Cristian Cosentino, University of Calabria, Italy
• Francesco De Luca, University of Calabria, Italy
• Simone Amirato, University of Calabria, Italy
• Eda Cicek, TU Wien, Austria
• Stefan Woltran, TU Wien, Austria
• Francescomaria Faticanti, École Normale Supérieure de Lyon, France
• Dakshine Ranjan Kisku, NIT Durgapur, India
• Sarada Prasad Gochhayat, IIT Jammu, India
• Bikash Chanda Singh, Old Dominion University, USA
• Simona Nisticò, University of Calabria, Italy
• Luca Ferragina, University of Calabria, Italy
4. Acknowledgments
We would like to express our sincere gratitude to everyone who contributed to the success of the 1st
Workshop on Green-Aware Artificial Intelligence.
We thank the Program Committee members and reviewers for their dedication in evaluating sub-
missions and providing constructive feedback. We also extend our appreciation to all authors who
submitted their research, as well as to the participants and attendees for their valuable discussions and
engagement during the workshop. A special acknowledgment goes to our invited speaker, Prof. Kees
van Berkel (TU Wien), for sharing his insightful perspectives on AI Alignment and Normative Reasoning,
as well as to the organizing committee of the 23rd International Conference of the Italian Association for
Artificial Intelligence. Finally, we acknowledge the support of the PNRR project FAIR - Future AI Research
(PE00000013), Spoke 9 - Green-aware AI, under the NRRP MUR program funded by NextGenerationEU.