Track Chairs:

Abdelsalam Busalim, School of Enterprise Computing and Digital Transformation, Technological University Dublin, Ireland
Email: abdelsalam.busalim@tudublin.ie

Fulya Acikgoz, University of Sussex Business School, University of Sussex, United Kingdom
Email: f.acikgoz@sussex.ac.uk

Track call:

Artificial Intelligence (AI) continues to reshape business, government, and society, but with its 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 significant energy consumption and carbon emissions associated with training and deploying large-scale AI systems (Verdecchia et al., 2023). This expanding footprint raises critical questions about the long-term sustainability of AI technologies. However, sustainability in AI extends beyond technical optimisation and environmental stewardship to encompass broader social, economic, and governance dimensions. This requires the design and deployment of AI systems that are not only energy-efficient and environmentally conscious, but also aligned with circular economy principles, equitable access, and long-term societal resilience (Bolón-Canedo et al., 2024).

At the same time, organisations, policymakers, and researchers face increasingly complex trade-offs between innovation, performance, regulatory compliance, value creation, fairness, and societal accountability (Papagiannidis et al., 2025). Important questions are emerging around how to balance AI accuracy and performance with explainability, transparency, fairness, and sustainability constraints (Liu and Vicente, 2020). The rapid emergence of alternative AI architectures, including smaller and more efficient language models (Lu et al., 2024), synthetic data approaches that address data accessibility, privacy, and fairness challenges (Lu et al., 2025), and broader resource-aware AI design strategies, presents important opportunities for rethinking sustainable innovation pathways.

This track welcomes critical and interdisciplinary perspectives that explore the dual imperative of sustainable and responsible AI. It seeks to facilitate dialogue on mitigating environmental impacts while addressing broader societal and organisational challenges, including social sustainability, inclusion, governance, trust, fairness, and responsible innovation. The track particularly welcomes contributions that critically examine the tensions, trade-offs, and practical implications of designing, governing, and deploying responsible AI at scale.

Track areas

The topics of interest in this track, but are not limited to:

  • Social sustainability challenges in the AI era
  • Trade-offs between innovation, value creation, regulation, AI performance, and accountability
  • Ethical and sustainable design, deployment, and use of AI systems
  • 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
  • Balancing fairness, accuracy, explainability, and performance in AI systems
  • The role of synthetic data in responsible and sustainable AI
  • Small language models and efficient AI architectures for sustainable innovation

References

  1. 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
  2. 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
  3. Liu, S., & Vicente, L. N. (2022). Accuracy and fairness trade-offs in machine learning: a stochastic multi-objective approach: S. Liu, LN Vicente. Computational Management Science, 19(3), 513-537.
  4. Lu, Z., Li, X., Cai, D., Yi, R., Liu, F., Zhang, X., … & Xu, M. (2024). Small language models: Survey, measurements, and insights. arXiv preprint arXiv:2409.15790.
  5. Papagiannidis, E., Mikalef, P., & Conboy, K. (2025). Responsible artificial intelligence governance: A review and research framework. The Journal of Strategic Information Systems, 34(2), 101885.
  6. 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