## 1 Introduction
For that, this study takes advantage of the latest developments in automatic machine learning (AutoML) tools [24] merged with Bayesian modeling [25; 26; 27] to provide an agile user experience in the calculation and assessment of environmental impacts. Practically, to carry out this study, a slate rock mine in the mining area of Quiroga in Galicia (north-western Spain) was chosen. Spain leads the world in producing and exporting natural slate, with a turnover in 2019 above 250 million euros, followed by China and Brazil [28; 29]. The slate mines are located in the so-called Truchas syncline (between Ourense and Leon), which gave rise to \"roofing slates\", namely a type of metamorphic slate with a very fine grain and, in general, dark tones, which, due to its intense planar foliation, can be exploited to obtain plates for roofs, flooring and pieces of masonry. Most of the existing mining designs are open-cast, with the slate layer being accessed by 4-8 m descending benches [30; 31]. From an environmental viewpoint, this process involves large-scale removal of overburden and the subsequent creation of slate dumps in nearby areas. In the future, the continuity of these open-pit mines would be conditioned by two critical factors: first, the excellent environmental protection of the surroundings, especially in this case where the mining area of Quiroga has a privileged place under the Natura 2000 Network, given that more than 35% of the surface of this municipality is included in the Special Area of Conservation (SAC) and Special Protection Area (SPA) for Birds, according to the European Union's Directives on Habitats and Conservation of Wild Birds, respectively [32; 33]; and second, the adequate transition towards underground mining operations as a means to maintain high mining extraction and productivity ratios. This article, in line with the industry agenda developed by the World Economic Forum and United Nation's 2030 Sustainable Development Goals (SDGs) [34; 35], describes the potential application of AI and AutoML to the mining and metals sector, outlining the key techniques and decision-making challenges that will shape its adoption in the transition towards sustainable mining. Concretely, as introduced during this section, this research addresses the particular challenge of conceiving an EIA that, making use of AI and AutoML techniques, is capable of reflecting in a dynamic manner the adverse impacts of mining activity in the deterioration of ecosystems, including the pollution generated by secondary processes, such as natural slate manufacturing. Section 2 explains the methodology of carrying out a holistic analysis of the impacts, by automatically learning Bayesian networks from the data conceptualizing the environmental variables under study. Section 3 presents the results obtained, the algorithms applied, and the overall learning and cloud computing process put in place to identify those environmental factors with greater susceptibility to mining processes in the study area. Section 4 argues how AI-based platforms equipped with Bayesian inference techniques can help engineers and environmental experts to identify adverse environmental conditions, gaining insights from conceptual (human-based) and mathematical (simulation-based) modeling as a solution to better understand uncertainty. Finally, Section 5 draws the main conclusions.