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Post-hoc explaining approaches for deep learning (DL) models has attracted much attention in safety–critical applications such as rotating machinery intelligent fault diagnosis (IFD). However, with the help of the explanation techniques, the models are still fragile to domain shifts caused by varying speeds and loads without help for improving their cross-domain performance. Since humans in the decision-making loop are essential for a reliable diagnostic system to determine the reliability of diagnosis, this paper proposes a causal explaining guided domain generalization (CXDG) method to realize trust worthy IFD with human in the decision loop. Specifically, an explaining model is trained with the conditional mutual information, which is a causal strength metric, and utilized to tell the causal features in the input data as the attributions of the diagnostic model. A translation process of the attributions is proposed to make the explaining process understandable. Furthermore, the aim of this paper is not only explaining but also beyond that the diagnostic model is guided to focus on the causal features to improve its generalization ability in unseen domains. The effectiveness of the method is validated on two experiment datasets. The results show that the proposed method can both explain the attributions of the diagnosis model and be beneficial to the generalization ability. [ABSTRACT FROM AUTHOR]

In recent years, the enormous development of Machine Learning, especially Deep Learning, has led to the widespread adoption of Artificial Intelligence (AI) systems in a large variety of contexts. Many of these systems provide excellent results but act as black-boxes. This can be accepted in various contexts, but there are others (e.g., medical ones) where a result returned by a system cannot be accepted without an explanation on how it was obtained. Explainable AI (XAI) is an area of AI well suited to explain the behavior of AI systems that act as black-boxes. In this paper, we propose a model-agnostic XAI framework to explain the behavior of classifiers. Our framework is based on network theory; thus, it is able to make use of the enormous amount of results that researchers in this area have discovered over time. Being network-based, our framework is completely different from the other model-agnostic XAI approaches. Furthermore, it is parameter-free and is able to handle heterogeneous features that may not even be independent of each other. Finally, it introduces the notion of dyscrasia that allows us to detect not only which features are important in a particular task but also how they interact with each other. • Performing model-agnostic Explainable AI on classifiers. • Introducing the concept of dyscrasia of feature occurrences. • Assessing the relevance of a feature during classification. [ABSTRACT FROM AUTHOR]

To deal with increasing amounts of data, decision and policymakers frequently turn to advances in machine learning and artificial intelligence to capitalise on the potential reward. But there is also a reluctance to trust black-box models, especially when such models are used to support decisions and policies that affect people directly, like those associated with transport and people's mobility. Recent developments focus on explainable artificial intelligence to bolster models' trustworthiness. In this paper, we demonstrate the use of an explainable-by-design model, Bayesian Networks, on travel behaviour. The model incorporates various demographic and socioeconomic variables to describe full day activity chains: activity and mode choice, as well as the activity and trip durations. More importantly, this paper shows how the model can be used to provide the most relevant explanation for people's observed travel behaviour. The overall goal is to show that model explanations can be quantified and, therefore, assist policymakers to truly make evidence-based decisions. This goal is achieved through two case studies to explain people's vulnerability as it pertains to their total trip duration. • Bayesian network approach to demonstrate explainable artificial intelligence (XAI). • Network structure includes demographic, travel and temporal variables. • Bayesian networks is both behaviourally rich and intuitively interpretable. • Quantify vulnerability explanations for travel behaviour. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is fast becoming a general purpose technology with outstanding impacts in industries worldwide, thus supporting the Industry 4.0 revolution. In particular, the energy sector is one of those that has taken more advantages from the implementation of AI approaches, especially Machine Learning models, for several applications, including energy performance benchmarking of buildings. However, the black-box approach could lead to a lack of result interpretability thus preventing the effective application of AI in some real-world scenarios. For this reason, eXplainable Artificial Intelligence (XAI) tools can be effectively embedded within an AI-based Energy Analytics methodology in order to enhance the explainability of the model results. In this paper, we propose an explainable AI-based benchmarking framework for estimating the membership to specific energy performance classes of a large set of Energy Performance Certificates (EPCs) of flats. The classification is obtained by leveraging different black-box classifiers characterized by high accuracy, yet their inference mechanism is not human-readable. Therefore, a generalizable XAI methodology, based on the combination of a local explainer together with a clustering algorithm, is employed to explain the model results and causal effects between the predictors and target variable to better understand the model behaviour, and the motivations behind correct and wrong performed classifications. The paper provides a general methodological approach capable to exploit a limited number of instances to extract, explain and interpret inference mechanisms learnt by the model that are useful for the end-user. The framework was tested on about 100,000 EPCs of flats located in Italy. • A data-driven benchmarking process of building energy performance is proposed. • A set of about 100,000 energy performance certificates of buildings is analysed. • Five classification algorithms are considered for developing the benchmarking tool. • Clustering analysis and XAI are coupled for extracting human-readable patterns. • Classification results are explained for understanding the model behaviour. [ABSTRACT FROM AUTHOR]

In the context of human-in-the-loop Machine Learning applications, like Decision Support Systems, interpretability approaches should provide actionable insights without making the users wait. In this paper, we propose Accelerated Model-agnostic Explanations (AcME), an interpretability approach that quickly provides feature importance scores both at the global and the local level. AcME can be applied a posteriori to each regression or classification model based on tabular data. Not only AcME computes feature ranking, but it also provides a what-if analysis tool to assess how changes in features values would affect model predictions. We evaluated the proposed approach on synthetic and real-world datasets, also in comparison with SHapley Additive exPlanations (SHAP), the approach we drew inspiration from, which is currently one of the state-of-the-art model-agnostic interpretability approaches. We achieved comparable results in terms of quality of produced explanations while reducing dramatically the computational time and providing consistent visualization for global and local interpretations. To foster research in this field, and for the sake of reproducibility, we also provide a repository with the code used for the experiments. • AcME, a new interpretability approach for Machine Learning, is proposed. • AcME is a model-agnostic, global and local approach. • AcME allows fast computational time w.r.t. to state-of-the-art approaches. • The effectiveness of AcME is demonstrated on both synthetic and real data. [ABSTRACT FROM AUTHOR]

• CASTLE aims to combine local and global features to explain any AI system predictions. • Model's global knowledge, as rules, supports the local explanation generation process. • CASTLE estimates how clusters' representative points in uence predictions. • It supports any explanation type (e.g. rule-based, feature importance, counterfactual). With Artificial Intelligence becoming part of a rapidly increasing number of industrial applications, more and more requirements about their transparency and trustworthiness are being demanded to AI systems, especially in military, medical and financial domains, where decisions have a huge impact on lives. In this paper, we propose a novel model-agnostic Explainable AI (XAI) technique, named Cluster-aided Space Transformation for Local Explanation (CASTLE) , able to provide rule-based explanations based on both the local and global model's workings, i.e. its detailed "knowledge" in the neighborhood of the target instance and its general knowledge on the training dataset, respectively. The framework has been evaluated on six datasets in terms of temporal efficiency, cluster quality and model significance. Eventually, we asked 36 users to evaluate the explainability of the framework, getting as result an increase of interpretability of 6 % with respect to another state-of-the-art technique, named Anchors. [ABSTRACT FROM AUTHOR]

• High-performance computing speeds up decision trees while consuming less memory. • Ensembled decision trees are explainable with a maximum satisfiability solver. • Ensembled decision trees can be verified against user specifications. This paper introduces a new high-performance machine learning tool named Silas, which is built to provide a more transparent, dependable and efficient data analytics service. We discuss the machine learning aspects of Silas and demonstrate the advantage of Silas in its predictive and computational performance. We show that several customised algorithms in Silas yield better predictions in a significantly shorter time compared to the state-of-the-art. Another focus of Silas is on providing a formal foundation of decision trees to support logical analysis and verification of learned prediction models. We illustrate the potential capabilities of the fusion of machine learning and logical reasoning by showcasing applications in three directions: formal verification of the prediction model against user specifications, training correct-by-construction models, and explaining the decision-making of predictions. [ABSTRACT FROM AUTHOR]