# Strategies of Automated Machine Learning for Energy Sustainability in Green Artificial Intelligence
## 7 Discussion  
In this section, we present the fundamental findings derived from our experiments with Red AI and Green AI. We conducted a thorough analysis of the various experimental results, focusing on HPO, and key metrics such as performance, energy efficiency, and sustainability. The evaluation of the carbon footprint across both datasets offers a quantitative assessment of environmental impact. By focusing on the minimization of energy consumption and the evaluation of the carbon footprint, Green AI experiments demonstrate a tangible improvement in sustainability. The presence of outliers adversely affects the means, resulting in median values that are typically higher compared to the mean values. These outliers could be hyperparameters of various algorithms that yield results significantly different from the majority of trials. In terms of validation accuracy, the PBT algorithm consistently exhibits median values that are higher than its means, particularly in Green AI scenarios. Most algorithms display median values that are lower than their means in \(CO_{2}e\) emissions, suggesting occasional spikes of high energy consumption. In terms of accuracy in Red AI and energy efficiency in Green AI, the BOHB algorithm demonstrates robust performance, with favorable median values. The differences between the 10 and 100 trial scenarios suggest that a larger number of trials allows AutoML algorithms to reveal more stable performance patterns. When selecting algorithms for hyperparameter optimization, it is important to consider both medians and means to gain a more comprehensive view of typical performance and result variability. The experimental results derived from both the Red AI and Green AI, specifically pertaining to \(CO_{2}e\) emissions, and both utilizing the CIFAR-10 dataset, were subjected to analysis using the Mann-Whitney U test. The choice of a non-parametric test is based on the violation of the assumptions of normality and equality of variances, which are considered critical for the validity of parametric tests [78; 79; 81]. The necessity of non-parametric alternatives like the Mann-Whitney U test is underscored in such instances. A U value of 92,407.0 was obtained, with a \(p\)-value of \(1.47\times 10^{-4}\). Given that this \(p\)-value is less than 0.05, it can be concluded that there are statistically significant differences between the two strategies in hyperparameter optimization (Figure 3). In the experimentation of the Red AI and Green AI strategies with the PBT and BOHB algorithms, using the IMDb dataset, the results do not meet the assumptions of normality and equality of variances. Therefore, the non-parametric Mann-Whitney U test was applied. In the case of the BOHB algorithm, the U value for \(CO_{2}e\) emissions is 280.0, with a \(p\)-value of 0.012, while for validation accuracy, U is 325.5 and the \(p\)-value is 0.067. These results indicate the presence of significant differences in \(CO_{2}e\) emissions between the Red AI and Green AI strategies (Figure 4). However, in terms of validation accuracy, we cannot assert that there are significant differences between both strategies. On the other hand, in the case of the PBT algorithm, the U value for \(CO_{2}e\) emissions is 589.0, with a \(p\)-value of 0.041, while for validation accuracy, U is 461.0 and the \(p\)-value is 0.877. Similar to the BOHB algorithm, these results indicate the presence of significant differences in \(CO_{2}e\) emissions between the Red AI and Green AI strategies. Nevertheless, in terms of validation accuracy, we cannot assert that there are significant differences between both strategies (Figure 5). Figure 4: The graph provides a visual representation of the distribution of the \(CO_{2}e\) metric in the context of Green and Red artificial intelligence (AI) strategies for the IMDb dataset for the PBT and BOHB algorithms. The figure shows the median and quartiles, providing a comprehensive view of the distribution of this metric in both AI strategies. Outliers resulting from the experimentation are also presented.