Workshop on Sustainable AI for the Future Web
AI has been widely integrated into web applications, and has revolutionized user experiences by enabling personalized services, optimizing decision-making and enhancing productivity. However, AI for the web application also poses significant sustainability challenges. AI models rely heavily on vast amounts of web data, consuming substantial computational resources and energy, while the proliferation of AI-generated content risks polluting the web with low-quality information and misinformation. This degradation of data quality threatens the robustness, fairness, and trustworthiness of AI systems, raising concerns about security, bias, and equitable outcomes across diverse user groups. Therefore, we propose this workshop that aims to advance research in sustainable AI for the web, focusing on energy-efficient, socially equitable, and technically robust AI models. By fostering interdisciplinary collaboration among AI, environmental science and social sciences experts, the workshop seeks to develop innovative solutions that ensure AI's long-term positive impact on the web.
Authors
- Johan Barthélemy (ORCID: https://orcid.org/0000-0002-7800-5309)
- Chang Xu (ORCID: https://orcid.org/0000-0002-4756-0609)
- Yasmeen George (ORCID: https://orcid.org/0000-0002-0977-0656)
- Jianyuan Guo (ORCID: https://orcid.org/0000-0002-2981-1953)
- Daochang Liu (ORCID: https://orcid.org/0000-0002-9279-7912)
- Ling Chen (ORCID: https://orcid.org/0000-0002-6468-5729)
- Yunke Wang (ORCID: https://orcid.org/0009-0003-9796-530X)
- Minjing Dong (ORCID: https://orcid.org/0009-0003-1717-818X)
- Yan Liu (ORCID: https://orcid.org/0000-0002-7055-9518)
Institutions
- University of Technology Sydney (AU)
- University of Southern California (US)
- The University of Sydney (AU)
- City University of Hong Kong (HK)
- The University of Western Australia (AU)
- University of Wollongong (AU)
- Monash University (AU)
Publication Details
- Published
- 2025-05-08
- DOI
- https://doi.org/10.1145/3701716.3717658
- Primary Topic
- Mobile Crowdsensing and Crowdsourcing
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Science Foundation
- University of Southern California
- Nvidia
- Carnegie Mellon University
- Institute for Catastrophic Loss Reduction
- University of Wollongong