Workshop on Sustainable AI for the Future Web

Chang Xu, Yunke Wang, Jianyuan Guo, Daochang Liu, Minjing Dong, Yasmeen George, Johan Barthelemy, Yan Liu, Ling Chen

Abstract: 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.

External IDs:dblp:conf/www/XuWGLDGBLC25