A robust methodology for long-term sustainability evaluation of Machine Learning models

📝 Original Info

• Title: A robust methodology for long-term sustainability evaluation of Machine Learning models
• ArXiv ID: 2511.08120
• Date: 2025-11-11
• Authors: 정보 없음 (제공된 데이터에 저자 정보가 포함되어 있지 않음)

📝 Abstract

Sustainability and efficiency have become essential considerations in the development and deployment of Artificial Intelligence systems, yet existing regulatory and reporting practices lack standardized, model-agnostic evaluation protocols. Current assessments often measure only short-term experimental resource usage and disproportionately emphasize batch learning settings, failing to reflect real-world, long-term AI lifecycles. In this work, we propose a comprehensive evaluation protocol for assessing the long-term sustainability of ML models, applicable to both batch and streaming learning scenarios. Through experiments on diverse classification tasks using a range of model types, we demonstrate that traditional static train-test evaluations do not reliably capture sustainability under evolving data and repeated model updates. Our results show that long-term sustainability varies significantly across models, and in many cases, higher environmental cost yields little performance benefit.

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