Workshop on Sustainable AI for the Future Web
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.030 | 0.010 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".