Track chairs
Abdelsalam Busalim, School of Enterprise Computing and Digital Transformation, Technological University Dublin, Ireland
Fulya Acikgoz, University of Sussex Business School, University of Sussex, UK

Track description
Artificial Intelligence (AI) continues to reshape business, government, and society, but with growing adoption comes increasing responsibility (Elia et al., 2025). As AI models become more complex and resource-intensive, concerns are mounting about their environmental impact—particularly the high energy consumption and resulting carbon emissions associated with training and deploying large-scale AI systems (Verdecchia et al., 2023). This growing footprint raises critical questions about the long term sustainability of AI technologies. Therefore, Sustainability in AI should go beyond technical optimization to encompass a holistic view that includes environmental stewardship, equitable access, and long-term societal resilience. This involves designing AI systems that are energy-efficient, environmentally conscious, and aligned with circular economy principles. (Bolón-Canedo et al., 2024).

This track welcomes critical, interdisciplinary perspectives that explore the dual imperative of sustainable and responsible AI. It seeks to facilitate dialogue on mitigating environmental impacts while upholding ethical principles throughout the AI lifecycle, addressing energy consumption, environmental footprint, and broader concerns of social and economic sustainability. Additionally, it emphasises responsibility, incorporating transparency, fairness, accountability, ethics, and a commitment to human centered values.

Track areas include but are not limited to:
– Ethical and sustainable design, deployment, and use of AI systems
– Intersectional approaches: sustainability, inclusion, ethics, and justice
– Governance frameworks and regulatory responses (e.g., EU AI Act, ISO standards, etc.,)
– Sociotechnical and organizational implications of Responsible AI
– Case studies on Sustainable AI across sectors (e.g., healthcare, energy, creative industries)
– Green AI, data minimisation, and carbon-aware computing
– Human-centered and value-sensitive design in AI
– Trust, bias mitigation, explainability, and accountability in practice

References
Bolón-Canedo, V., Morán-Fernández, L., Cancela, B., & Alonso-Betanzos, A. (2024). A review of green artificial intelligence: Towards a more sustainable future. Neurocomputing. https://doi.org/10.1016/j.neucom.2024.128096

Verdecchia, R., Sallou, J. and Cruz, L., 2023. A systematic review of Green AI. arXiv preprint, arXiv:2301.11047. Available at: https://arxiv.org/abs/2301.11047

Elia, M., Ziethmann, P., Krumme, J. et al. Responsible AI, ethics, and the AI lifecycle: how to consider the human influence?. AI Ethics (2025). https://doi.org/10.1007/s43681-025-00666-z