# Semantic Description of Explainable Machine Learning Workflows for Improving Trust
## 1 Introduction  
Artificial Intelligence (AI) and particularly Machine Learning (ML) have been extensively explored due to their ability to learn and perform autonomous tasks, and the potential to achieve better results than humans [1, 2]. Among ML models, there are inherently intelligible algorithms, as opposed to inscrutable ones. Models are inherently intelligible to the degree that a human can predict how a change to a feature in the input can affect the output [2]. Inscrutable models are more complex and harder to explain, therefore it is more challenging to understand the reason for their results. Examples are complex neural networks or deep learning. For this reason, these models are often considered black-boxes [3]. To cope with this, Explainable Artificial Intelligence (XAI) considers methods and techniques to make the results of AI systems explainable, intelligible, transparent, interpretable, or comprehensible to humans [1]. ML explainability is relevant because it allows the identification of the changes and optimization of the ML model necessary to generate the results, since being able to understand the model allows us to identify problems and improve the model. This ensures that the system acts adequately, improving trust and avoiding unethical issues [3]. Semantic Web Technologies (SWT) were initially introduced to make the internet data machine-readable by encoding semantics with the data. In the scope of XAI, these techniques can potentially be applied to ML models and are expected to enable the development of truly explainable AI-systems because they provide semantically interpretable tools and allow reasoning on knowledge resources that can help explain ML systems [4, 5, 6]. They usually are adopted as complementary sources of information that enrich the datasets with semantic knowledge, enabling the exploitation of the relationships between concepts and inferences of new knowledge [1]. There are two main categories of exploration approaches regarding how explanations are generated by considering the part of the machine learning process that is using the semantic resource, namely (1) ante-hoc and (2) post-hoc, as schematically depicted in Figure 1.  
Ante-hoc explainability builds an intrinsic explainable model, using semantic resources during the ML training process to build explainable learning models that generate predictions together with explanations of its reasoning. In this case, the semantic source is integrated intrinsically to the ML algorithm to obtain explanations considering the internal functioning of the model by mirroring the structure of knowledge graphs, using knowledge resources as embeddings, or exploring the ontology taxonomy, among others. The transparency of the ante-hoc model facilitates understanding and enables adequate changes in the ML when necessary [1]. However, changes are necessary to incorporate the background knowledge to the algorithms, forcing design choices and creating a bias towards explainability, which can affect the performance of existing models regarding accuracy and efficiency. Consequently, the solutions are often model-specific and domain-specific, resulting in less generalizable and versatile outcomes [7]. Post-hoc explainability consists of wrapping fully black-box trained models and adding an explainability layer [8]. The biggest advantage in post-hoc solutions is that they are model-agnostic, that is, the explanations are separated from the ML model, thus no change is needed to the ML model so that the solutions can be used across different models. Post-hoc solutions explain the logic of the output by trying to justify the reason why the ML model generates the results. The use of SWT empowers the explainable ML tools by expanding their knowledge without requiring prior experience, creating explanations for patterns or questions that go beyond the data analyzed [1].