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

TL;DR

This paper introduces a new, standardized evaluation protocol inspired by Online ML to assess the long-term environmental sustainability of ML models, addressing limitations of current practices.

Contribution

It proposes a novel, model-agnostic sustainability assessment method based on incremental retraining, improving long-term impact estimation of ML models.

Findings

Traditional static evaluations misestimate sustainability costs.

Incremental retraining better captures real-world environmental impact.

Higher environmental costs may not always improve performance.

Abstract

Sustainability and efficiency have become essential considerations in the development and deployment of Artificial Intelligence systems, but existing regulatory practices for Green AI still lack standardized, model-agnostic evaluation protocols. Recently, sustainability auditing pipelines for ML and usual practices by researchers show three main pitfalls: 1) they disproportionally emphasize epoch/batch learning settings, 2) they do not formally model the long-term sustainability cost of adapting and re-training models, and 3) they effectively measure the sustainability of sterile experiments, instead of estimating the environmental impact of real-world, long-term AI lifecycles. In this work, we propose a novel evaluation protocol for assessing the long-term sustainability of ML models, based on concepts inspired by Online ML, which measures sustainability and performance through incremental/continual model retraining parallel to real-world data acquisition. Through experimentation on diverse ML tasks using a range of model types, we demonstrate that traditional static train-test evaluations do not reliably capture sustainability under evolving datasets, as they overestimate, underestimate and/or erratically estimate the actual cost of maintaining and updating ML models. Our proposed sustainability evaluation pipeline also draws initial evidence that, in real-world, long-term ML life-cycles, higher environmental costs occasionally yield little to no performance benefits.