## 4 Discussion  
In the present case study for a natural slate mine in the mining region of Quiroga (Spain), an information theory analysis based on unsupervised Bayesian network allowed for identifying the factors _Surface and Subsurface Runoff, Shapes and Volumes, Species or Individual Concentration_ and _Soil Degradation_ as the possible nodes with the greatest environmental impact on the study area. In order to facilitate decision-making in situations in which there is uncertainty, using Bayesian inference techniques, a medium-low environmental impact index (3.839) was obtained with a degree of uncertainty equal to 27.079% (Figure 5).  
Likewise, once the conceptual and mathematical relationships associated with the _Surface Waters_ cluster were identified, three study scenarios were assessed based on the level of alteration of the contributions made to the basin. Through Bayesian inferential analysis, it was possible to better understand the uncertainty and observe the great variation in the percentage distributions of the environmental factors of interest related to this factor.  
In Figure 6 are presented three scenarios (1--compatible, 2--moderate and 3--severe-critical) for which the variation in _Basin Contribution Changes_ notably affects the environmental conditions of other attributes. For example, it is surprising how _Flood Zones Alteration_ or _Current Flow Variation_ are completely coupled to _Basin Contribution Changes_, presenting the same results. However, _Flow Changes_ shows a more uncertain behavior, where in scenario 2 shows a 50% between compatible and moderate.  
In this context, it is fundamental to remark that this inference analysis corresponds to a relatively small part of the whole network (Figures 2 and 3). The analysis possibilities are almost limitless and depend on a good understanding of the study. In this respect, it is recognized how water may influence the environmental risk of natural slate mines [31; 37].  
This study shows the great potential of AutoML and Bayesian networks to reason under uncertainty, bringing flexibility to decision making. In particular, the results prove the value of information (VoI) obtained with a model-based simulation that excels simple human-based approaches that are still widely used nowadays [22; 23; 47]. Therefore, it is possible to achieve statistical evidence of the heterogeneity of the circumstances established in the definition of the EIA index. In the coming years, the implementations of AI-based platform solutions as part of the overall digitalization of the mining and metals industry should contribute to the development of sustainable mining.  
Initiating dialogue between mining companies, policy makers and environmental organizations is urgent [34; 48]. Areas of special natural interest, such as the one presented in this study, where a mine is completely surrounded by the Natura 2000 Network (Figure 1), are not an exception. In fact, in the future, these types of situations are only expected to grow in prevalence due to the increasing policy activity of legislators on biodiversity. Undoubtedly, this is a positive aspect. At the same time, mining activity is necessary and should be further stimulated in a social context where raw materials demand increases relentlessly to meet consumer and industrial needs. This article shows how a new technological paradigm supported by AI can scientifically help demonstrate or debunk the negative impact of mining activity, defeating lobbies and interested groups and bringing finally certainty to a sector, which has traditionally suffered from volatile geopolitical events.