With the growing availability of large-scale datasets, and the popularization
of affordable storage and computational capabilities, the energy consumed by AI
is becoming a growing concern. To address this issue, in recent years, studies
have focused on demonstrating how AI energy efficiency can be improved by
tuning the model training strategy. Nevertheless, how modifications applied to
datasets can impact the energy consumption of AI is still an open question. To
fill this gap, in this exploratory study, we evaluate if data-centric
approaches can be utilized to improve AI energy efficiency. To achieve our
goal, we conduct an empirical experiment, executed by considering 6 different
AI algorithms, a dataset comprising 5,574 data points, and two dataset
modifications (number of data points and number of features). Our results show
evidence that, by exclusively conducting modifications on datasets, energy
consumption can be drastically reduced (up to 92.16%), often at the cost of a
negligible or even absent accuracy decline. As additional introductory results,
we demonstrate how, by exclusively changing the algorithm used, energy savings
up to two orders of magnitude can be achieved. In conclusion, this exploratory
investigation empirically demonstrates the importance of applying data-centric
techniques to improve AI energy efficiency. Our results call for a research
agenda that focuses on data-centric techniques, to further enable and
democratize Green AI.

本文旨在通过实证研究来评估数据集修改对人工智能能源效率的影响。实验结果表明，可以通过仅对数据集进行修改（数据点数和特征数），显著降低（高达 92.16%）能耗并几乎不降低准确性。此外，本文还证明了通过更改算法能够实现节能（高达两个数量级）。研究成果呼吁进一步研究采用数据为中心技术，进一步推动绿色人工智能的普及。