# Improving Automated Machine-Learning Systems through Green AI
###### Abstract  
Automated machine learning (AutoML), which aims to facilitate the design and optimization of machine-learning models with reduced human effort and expertise, is a research field with significant potential to drive the development of artificial intelligence in science and industry. However, AutoML also poses challenges due to its resource and energy consumption and environmental impact, aspects that have often been overlooked. This paper predominantly centers on the sustainability implications arising from computational processes within the realm of AutoML. Within this study, a proof of concept has been conducted using the widely adopted Scikit-learn library. Energy efficiency metrics have been employed to fine-tune hyperparameters in both Bayesian and random search strategies, with the goal of enhancing the environmental footprint. These findings suggest that AutoML can be rendered more sustainable by thoughtfully considering the energy efficiency of computational processes. The obtained results from the experimentation are promising and align with the framework of Green AI, a paradigm aiming to enhance the ecological footprint of the entire AutoML process. The most suitable proposal for the studied problem, guided by the proposed metrics, has been identified, with potential generalizability to other analogous problems. green software engineering; AutoML; energy efficiency; Green AI; sustainability; carbon footprint; ecological footprint; CO2e +
Footnote †: journal: Applied  
performance before full implementation. With this approach in mind, we identify the main challenges and opportunities for integrating green computing techniques into the ML pipeline, such as data preprocessing, model selection, hyperparameter tuning, and deployment. We also discuss some potential applications and benefits of green machine-learning systems for different domains, such as healthcare, education, and smart cities. To conclude this introduction, we outline some open research directions and future perspectives for this novel and promising area of research [9; 10]. Although artificial intelligence (AI) and AutoML are known for their potential to contribute to sustainability, the sustainability of AI and AutoML has not received the same level of attention. Building upon this premise, the concept of Green AI was introduced by [PERSON] et al. [11] and is an emerging field that focuses on the development of AI algorithms and implementations that are more resource-efficient. This is important because AI has a significant environmental impact, as it requires a large amount of energy to train and run models. In [12; 13; 14; 15], the carbon footprint of AI computing has been characterized by examining the model development cycle across industry-scale machine-learning use cases while also considering the life cycle of system hardware. This comprehensive perspective underscores the need for further investigation into the sustainability of AI, particularly in terms of economic, social, and environmental aspects. It is necessary to emphasize the importance of thoroughly assessing our ecological impact and actively avoiding unnecessary and extensive carbon footprints, firmly anchoring our proposals within the realm of sustainability. At the core of our proposal is the integration of Green AI metrics or accurate measurements of energy consumption into the intricate algorithms of AutoML. By incorporating these pivotal metrics, we chart an innovative path toward significantly reducing the inherent carbon emissions in machine-learning processes. This strategic alignment of AutoML with Green AI culminates in a collective stride towards a more environmentally conscious future. The field of AutoML is inherently complex and not readily amenable to theoretical analysis, thus making empirical research contributions predominant. This necessitates conducting extensive experimental studies, which consume a substantial amount of computational resources and subsequently result in considerable carbon emissions.