"Should I choose a smaller model?'': Understanding ML Model Selection and Its Impact on Sustainability

要旨

The increasing accessibility of large machine learning (ML) models has resulted in their widespread adoption in everyday products, with a correspondingly negative environmental impact. Selecting more suitable ML models could not only improve training time and achievable accuracy, but also long-term sustainability. However, ML developers' model selection process remains underexplored, especially with respect to sustainability trade-offs. Our interviews with 13 ML developers showed that participants select models mainly based on familiarity, accuracy and interpretability, but often overlook sustainability. They critically reflected on the current trends of large models and the lack of available information regarding model sustainability. We present implications for the ML and HCI communities, emphasizing the importance of critical reflection on model selection in education and practice. Based on our insights, we provide initial recommendations for promoting model sustainability evaluation and how the HCI community can assist in making sustainable model alternatives more accessible.

著者
Eya Ben chaaben
Inria Paris Saclay, Paris, France
Janin Koch
UMR 9189 CRIStAL, Lille, France
Wendy E.. Mackay
Inria, Paris, France
DOI

10.1145/3706598.3713240

論文URL

https://dl.acm.org/doi/10.1145/3706598.3713240

動画

会議: CHI 2025

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2025.acm.org/)

セッション: Sustainable Individual, Society, and Environment

Annex Hall F205
7 件の発表
2025-04-28 23:10:00
2025-04-29 00:40:00
日本語まとめ
読み込み中…