Model interpretability is mainly related to the complexity of ML that is used to build the model. Generally speaking, models with more complex ML algorithms are more challenging to interpret and explain. In contrast, models with more straightforward ML algorithms are easiest to interpret and explain [20]. Therefore, ML models can be categorized into two criteria: intrinsic or post hoc.

Intrinsic interpretability model uses transparent ML model, such as sparse linear models or short decision trees. Interpretability is achieved by limiting the ML model complexity [21] in such a way that the output is understandable by humans. Unfortunately, in most cases, the best performance of ML models belongs to very complex ML algorithms. In other words, model accuracy requires more complex ML algorithms, and simple ML algorithms might not make the most accurate prediction. Therefore, intrinsic models are suitable when interpretability is more important than accuracy.

In contrast, post hoc interpretability model is used to explain complex/black-box ML models. After the ML models have been trained, the interpretation method is applied to extract information from the trained ML model without precisely depending on how they work [22].

Model-related interpretability model uses any ML algorithms (model-agnostic) or only a specific type or class of algorithm (model-specific). Model-specific is limited to a particular model or class in such a way that the output explanations are derived by examining the internal model parameters, such as interpreting the regression weights in a linear model [9].

Model-agnostic interpretability models are not restricted to a specific ML model. In these models, explanations are generated from comparing input and output features at the same time. This approach does not necessarily access the internal model structure such as the model weights or structural information [7]. Usually, a surrogate or a basic proxy model is used to achieve model-agnostic interpretability. The surrogate model is a transparent ML model that can learn to approximate or mimic a complex black-box model using the black-box’s outputs [21]. Surrogate models are commonly used in engineering. The idea is to create a simulation model that is cheap, fast, and simple to mimic the behaviours of the expensive, time-consuming, or complex model that is difficult to measure [23]. Model-agnostic methods separate the ML model from the interpretation method, giving the developer the flexibility to switch between models with low cost.

For a better understanding of the model-related taxonomy, Table 1 provides a summary of the pros and cons of each category.

The scope of the IML explanations depends on whether the IML model applies to a specific sample to understand the prediction of the applied ML in this sample or applies to the entire model samples in attempts to explain the whole model behaviours. Therefore, methods that describe a single prediction are considered as local interpretation, and methods that describe the whole action of the model are considered as global interpretation [7].

Local interpretation refers to an individual explanation that justifies why the model generated a specific prediction for a specific sample. This interpretability aims to explain the relationship between a particular input data and the generated prediction. It is possible to obtain a local understanding of the models by constructing justified model structures. This can also be done by presenting identical examples of instances to the target instance [9]. For example, by highlighting a patient’s unique features identical to those of a smaller group of patients, they are distinct from other patients.

Global interpretation, however, provides transparency about how the model works at an abstract level. This kind of interpretability aims to figure out how the model generates decisions based on a comprehensive view of its features and learned individual components such as weights, structures, and other parameters. A trained model, algorithm, and data information are required to understand the model’s global performance of the model [22].

Based on complexity, complexity-related ML models could be intrinsic or post hoc. Intrinsic models interpret ML models by restricting the ML complexity, i.e., the maximum depth of the Decision Trees algorithm must be at least 5 to be understandable. Applying such roles to interpret a specific model makes intrinsic models by default considered model-specific. Model-specific techniques, in general, are techniques that only apply to the interpretation of a particular ML model. The model-specific explanation output can be generated to explain a specific prediction for a specific sample (local interpretation) or to comprehend the model’s decisions from a holistic perspective (global interpretation).

Post hoc models, on the other hand, are related to interpretation methods used after the ML model has been trained. Post hoc interpretation methods can be either model-specific or model-agnostic according to the domain of the applied interpretable method, whether it is for a specific ML model (model-specific) or applied to all ML models (model-agnostic).

In the next section, we will give an overview of interpretation in IML, its properties, and outcomes.

The term interpret explains the meaning of something in an understandable way. Instead of general interpretability, we concentrate on using interpretations as part of the broader data science life cycle in ML. The terms interpretability and explainability are being used interchangeably in literature [24,25], but there are some papers that make distinctions [26,27]. Rudin and Ertekin [27] argue that the term explainable ML is used to explain black-box models whereas interpretable ML is used for models that are not black-box. Gaur et al. [26] argue that explainability answers the question of why a certain prediction has been made whereas interpretability answers the question of how the model predicts a certain prediction. Our paper follows the first approach, where the terms interpretability and explainability are used interchangeably. However, the formal definition of interpretability remains elusive in machine learning as there is no mathematical definition [28].

A popular definition of interpretability frequently used by researchers [29,30] is interpretability in machine learning is a degree to which a human can understand the cause of a decision from an ML model. It can also be defined as the ability to explain the model outcome in understandable ways to a human [31]. The authors of [32] defined interpretability as the use of machine learning models to extract specific data-contained information of domain relationships. If it gives insight into a selected domain issue for a specific audience; they see information as necessary. [12] describe the primary purpose of interpretability as being to effectively explain the model structure to users. Depending on the context and audience, explanations may be generated in formats such as mathematical equations, visualizations, or natural language [33]. The primary goal of interpretability is to explain the model outcomes in a way that is easy for users to understand. IML consists of several main components: (1) the machine learning model, (2) the interpretation methods, and (3) the outcomes. The interpretation methods exhibit some properties as described in the following subsections.

This section presents the properties needed to judge how good the interpretation method is. Based on the conducted review, we identified the essential proprieties as explained below.

The main goal of using an interpretable ML model is to explain the outcome of the ML model to users in an understandable way. Based on the interpretable method used [7,28], the interpretation outcome can be roughly distinguished into one of the following stages:

-Feature summary statistics: ML model outcomes describing statistic summary for each feature. The statistic summary contains a single number for each feature, such as feature importance, or a single number for each couple of features, such as pairwise feature interaction strengths.-Feature summary visualization: Visualizing the feature summary is one of the most popular methods. It provides a visualization in form of graphs representing the impact of the feature to the ML model prediction.