# _Separtial system innovation_
## 3 Theoretical Framework on eXplainable Artificial Intelligence
These techniques are usually linked to models (such as tree ensembles, support vector machines, multi-layer neural networks, convolutional neural networks, recurrent neural networks, and similar) whose inference mechanism is unknown to users. Post-hoc techniques can be used with any AI model because they are often model-agnostic. In terms of model-agnostic post-hoc methodologies, several have been developed recently to improve the explainability of AI models using data science, ML, and statistics techniques [41]. Four broad categories may be used to group these techniques: example-based explanation, impact approaches, information extraction, and visualization. * Visualization: This technique uses representations of an ML model, such as a DN, which is a natural way to look at the pattern hidden inside a cell. Three well-known visualization techniques are individual conditional expectation (ICE), partial dependence plot (PDP), and surrogate models:
* More complex models are described by surrogate models, which are simple models. In order to understand the latter, a trainable model--like a decision tree or linear model--is trained using the original black-box model's predictions;
* A graphical representation known as PDP facilitates the visualization of an averaged partial relationship between one or more input variables and the predictions of a black-box model;
* While PD charts provide a rough overview of a model's activities, ICE graphs disaggregate the PDP output to show interactions and individual differences. * Knowledge extraction is the process of obtaining, in an intelligible way, the information that the algorithm records as an internal representation during training. When considering an ANN, knowledge extraction confronts the difficulty of extracting explanations from the network. Rule extraction and model distillation are two kinds of techniques for obtaining information concealed in complex algorithms:
* rule extraction provides a symbolic and understandable explanation of the data the algorithm has learned during training by utilizing its input and output to extract rules that mimic the decision-making process;
* model distillation is a compression technique used to transport information (dark knowledge) from deep networks (the \"teacher\") to shallow networks. * Influence methods: This approach assesses the importance or relevance of a feature by altering internal or input components and documenting the extent to which the changes affect model performance. The use of influence tactics is common. Three methods for assessing the significance of an input variable are feature importance, sensitivity analysis, and Layer-wise Relevance Propagation (LRP)sensitivity analysis explains how the output is affected by changes in its input and/or algorithm parameters. It is frequently used to test models for stability and reliability, either as a tool to identify and eliminate irrelevant input attributes or as a foundation for a more potent explanation technique (e.g., decomposition)
* LRP redistributes the prediction function backwards, backpropagating up to the input layer from the network output layer. One important component of this redistribution process is relevance conservation. * Feature importance measures how much each feature, or input variable, contributes to a complicated AI/ML model's predictions. The rise in the model prediction error following feature permutation is used to assess a feature's importance. As the values of important characteristics are changed, the model error rises. To maintain a consistent model error throughout permutation, the model disregards the values of unimportant attributes. * Example-based explanation: In accordance with this paradigm, the practitioner describes the behavior of AI/ML models by selecting particular examples from the dataset.