# Contextual Explanations for Decision Support in Predictive Maintenance
## 2 Related Work  
This work focusses on explainable artificial intelligence methods and their application in decision support systems, particularly within industrial contexts involving time series data. To provide context, an overview of the relevant literature related to these themes is presented below. The explainable artificial intelligence methods have been extensively analysed, compared and classified in numerous reviews and surveys [3; 9; 10; 12; 13; 14; 15; 16; 17; 18]. These studies analyse the XAI domain identifying various classes of approaches, e.g., model-specific and model-agnostic, or global and local explanations. From the perspective of this study, model-agnostic methods giving local explanations are of particular interest. There are several methods of this type. Counterfactual explanations [19] highlight what should be changed in a data instance in order to receive a different output. Ceteris paribus profiles (also named individual conditional expectations) [9; 20] present the impact of a given attribute on the model's decision. Various methods determine which attributes are the most important to the model during the decision-making process for a given data instance. SHAP [21] refers to coalitional game theory. LIME [22] is based on locally generated linear models. RuleXAI [23] uses a surrogate rule-based model and evaluates the impact of individual rule conditions (related to the data attributes and their values) on the model's decision. The XAI methods characterised above can be applied to various types of data, including time series, which can be treated as tabular data, particularly when derived variables characterising the time series are generated [11; 24]. However, in addition to the general XAI methods, other solutions dedicated to time series have been developed. Several aspects of XAI methods on time series were discussed in [25], e.g., the global and local scale of explanations, their purpose and intended audience and evaluation approaches. Among the methods presented, four types were distinguished. The first two types were related to neural networks. For convolutional neural networks, methods based on backpropagation and perturbation were discussed, whereas for recurrent neural networks attention mechanisms were considered. The third type of approaches were called data mining based methods. They included the approaches based on symbolic aggregate approximation (SAX) [26] and fuzzy logic. The last mentioned type was based on representative examples, that is, on Shapelets [27]. A slightly different set of XAI methods for time series was presented in [28], where a perturbation-based method for generating explanations was discussed. The identified classes of methods included Shapelets, Saliency and Counter-examples. A natural example of an IT system where it is crucial to provide clear explanations for suggested decisions is a decision support system (DSS). Decision support systems using XAI are particularly often discussed in the literature, particularly within medical applications [3; 4; 5]. This is understandable, as one of the most essential features that should characterise medical DSS is trustworthiness, which is one of the goals of XAI [13]. Among the approaches to the design of medical decision support systems, work on human-centred XAI [29] should also be mentioned. This work introduces the approach based on three components: domain analysis, requirements elicitation and assessment, and multi-modal interaction design and evaluation. Moreover, a set of explanation design patterns was proposed in this work. The use of explanations is becoming increasingly common for AI solutions used in industry. An interesting survey related to the domain investigated in this study is presented in [7]. It is focussed on applications of XAI methods for identifying or predicting events related to predictive maintenance. The survey presents several conclusions regarding the state of research on XAI methods from the perspective of predictive maintenance.