## Diagram: Sequence-based Interpretation of XAI ### Overview The image is a diagram illustrating the sequence-based interpretation of Explainable Artificial Intelligence (XAI). It outlines the process through pre-modelling, in-modelling, and post-modelling stages, detailing various methods and techniques associated with each stage. ### Components/Axes * **Central Node:** "Sequence-based interpretation of XAI" (orange box) * **Stages:** * Pre-modelling (green box, top-left) * In-modelling (green box, bottom-left) * Post-modelling (green box, right) * **Pre-modelling Sub-components:** * Data collection (green box) * Data clarification (green box) * By design (green box) * **In-modelling Sub-components:** * Model-based interpretable methods (green box) * Attentional self-interpretation combine prediction and explanation (green box) * **Post-modelling Sub-components:** * Visualisation (green box) * Surrogate model (green box) * Control variable (green box) * Interactive method (green box) * Knowledge extraction (green box) * Rule extraction (green box) * Teaching rule extraction (green box) * Decomposition rule extraction (green box) * Compromise rule extraction (green box) * Model distillation (green box) * Impact level methods (green box) * Sensitivity analysis (green box) * Layered correlation communications (green box) * Characteristic importance (green box) * Instance-based samples (green box) * Counterfactual explanations (green box) ### Detailed Analysis The diagram presents a sequential flow, starting with pre-modelling, moving to in-modelling, and culminating in post-modelling. Each stage is connected to the central node "Sequence-based interpretation of XAI" via curved arrows. Each stage has sub-components, which are connected via straight arrows. * **Pre-modelling:** This stage focuses on preparing the data and defining the design for the XAI process. It includes data collection, data clarification, and design considerations. * **In-modelling:** This stage involves the actual modelling process, emphasizing interpretable methods and self-interpretation techniques that combine prediction and explanation. * **Post-modelling:** This stage focuses on extracting knowledge and insights from the model. It includes visualization techniques (surrogate models, control variables, interactive methods), knowledge extraction (rule extraction, model distillation), impact level methods (sensitivity analysis, layered correlation communications, characteristic importance), and instance-based samples (counterfactual explanations). ### Key Observations * The diagram provides a structured overview of the XAI interpretation process. * It highlights the importance of each stage, from data preparation to knowledge extraction. * The diagram emphasizes the use of various methods and techniques to enhance the interpretability of AI models. ### Interpretation The diagram illustrates a comprehensive approach to XAI, emphasizing the importance of a sequence-based interpretation. It suggests that effective XAI requires careful consideration of data preparation, model building, and post-hoc analysis. The diagram highlights the interconnectedness of these stages and the various methods that can be employed to enhance the interpretability of AI models. The inclusion of specific techniques like surrogate models, rule extraction, and sensitivity analysis suggests a focus on making AI models more transparent and understandable to users.

\n ## Diagram: Sequence-based Interpretation of XAI ### Overview The image is a diagram illustrating the sequence-based interpretation of Explainable Artificial Intelligence (XAI). It depicts a flow chart with three main stages: Pre-modelling, In-modelling, and Post-modelling, connected to the central concept of "Sequence-based Interpretation of XAI". Each stage branches out into several methods or techniques. ### Components/Axes The diagram is organized around a central node labeled "Sequence-based Interpretation of XAI". The three main branches are: * **Pre-modelling:** Located on the left side of the central node. * **In-modelling:** Located on the lower-left side of the central node. * **Post-modelling:** Located on the right side of the central node. Each branch then splits into sub-branches representing specific techniques. ### Detailed Analysis or Content Details **Pre-modelling:** * Data collection * Data clarification * By design **In-modelling:** * Model-based interpretable methods * Attention-based self-interpretation combine prediction and explanation **Post-modelling:** * **Visualization:** * Surrogate model * Control variable * Interactive method * **Knowledge extraction:** * Rule extraction * Teaching rule extraction * Decomposition rule extraction * Compromise rule extraction * Model distillation * **Impact level methods:** * Sensitivity analysis * Layered correlation communications * Characteristic importance * **Instance-based samples:** * Counterfactual explanations The connections between the stages and techniques are represented by arrows indicating the flow of information or process. The central node "Sequence-based Interpretation of XAI" is highlighted in orange, while the other nodes are green. ### Key Observations The diagram emphasizes a sequential approach to XAI interpretation, starting with data preparation (Pre-modelling), moving through model building (In-modelling), and culminating in analysis and explanation (Post-modelling). The Post-modelling stage is the most complex, with four distinct branches representing different methods for understanding and interpreting the model's behavior. The hierarchical structure within the "Knowledge extraction" branch suggests a refinement process, moving from general rule extraction to more specific techniques. ### Interpretation This diagram illustrates a comprehensive framework for interpreting XAI models. It suggests that a successful XAI strategy requires attention to all stages of the model lifecycle, from data collection to post-hoc analysis. The branching structure of the Post-modelling stage indicates that there is no single "best" method for XAI interpretation; rather, the appropriate technique depends on the specific goals and context of the application. The diagram highlights the importance of both global (e.g., rule extraction, model distillation) and local (e.g., counterfactual explanations) interpretability methods. The emphasis on visualization suggests that effective communication of XAI results is crucial for building trust and understanding. The diagram is a high-level overview and does not provide specific details about the implementation of each technique. It serves as a conceptual map for navigating the complex landscape of XAI interpretation.

## Diagram: Sequence-based Interpretation of XAI (Explainable Artificial Intelligence) ### Overview The image is a hierarchical flowchart or mind map illustrating a taxonomy of methods for interpreting Explainable AI (XAI). The central concept is "Sequence-based interpretation of XAI," which branches into three primary phases of the modeling pipeline: Pre-modelling, In-modelling, and Post-modelling. Each phase contains specific categories and sub-categories of interpretive techniques. ### Components/Structure The diagram is structured as a tree with a central node and three main branches extending left and right. * **Central Node (Orange Box):** "Sequence-based interpretation of XAI" * **Primary Branches (Green Boxes):** * **Left, Upper:** "Pre-modelling" * **Left, Lower:** "In-modelling" * **Right:** "Post-modelling" * **Secondary & Tertiary Branches (Light Green Boxes):** These extend from the primary branches, detailing specific methods. Connections are shown with directional arrows. ### Detailed Analysis **1. Pre-modelling Branch (Left, Upper):** This branch lists methods applied before model training. * **Sub-nodes connected to "Pre-modelling":** * "Data collection" * "Data clarification" * "By design" **2. In-modelling Branch (Left, Lower):** This branch lists methods integrated into the model's architecture or training process. * **Sub-nodes connected to "In-modelling":** * "Model-based interpretable methods" * "Attentional self-interpretation combine prediction and explanation" (Note: This is a single, lengthy label). **3. Post-modelling Branch (Right):** This is the most extensive branch, detailing methods applied after a model is trained. It splits into four main categories. * **Category 1: "Visualisation"** * "Surrogate model" * "Control variable" * "Interactive method" * **Category 2: "Knowledge extraction"** * "Rule extraction" * "Teaching rule extraction" * "Decomposition rule extraction" * "Compromise rule extraction" * "Model distillation" * **Category 3: "Impact level methods"** * "Sensitivity analysis" * "Layered correlation communications" * "Characteristic importance" * **Category 4: "Instance-based samples"** * "Counterfactual explanations" ### Key Observations * **Taxonomy by Pipeline Stage:** The primary organizational principle is the sequence of the machine learning workflow (Pre, In, Post), not the technical approach of the XAI method itself. * **Asymmetry in Detail:** The "Post-modelling" branch is significantly more detailed than the others, suggesting a greater variety or emphasis on techniques applied after model training in this framework. * **Hierarchical Depth:** The "Knowledge extraction" category under "Post-modelling" has the deepest hierarchy, with three distinct types of "rule extraction" specified. * **Label Specificity:** Some labels are very specific (e.g., "Attentional self-interpretation combine prediction and explanation"), while others are broad categories (e.g., "Visualisation"). ### Interpretation This diagram presents a structured, process-oriented framework for categorizing XAI techniques. It argues that interpretability is not a single step but a concern to be addressed throughout the entire model development lifecycle. * **Pre-modelling** focuses on **foundational transparency**—ensuring the data and design choices are sound and understandable from the start. * **In-modelling** focuses on **intrinsic interpretability**—building models whose internal mechanisms are inherently easier to understand (like decision trees) or incorporating explanation generation directly into the model's operation (like attention mechanisms). * **Post-modelling** focuses on **post-hoc explanation**—applying external tools to a trained "black-box" model to probe its behavior, extract simplified rules, visualize its decision boundaries, or explain individual predictions (e.g., via counterfactuals). The depth in the "Post-modelling" section reflects the current research and application landscape, where significant effort is devoted to explaining complex, pre-existing models. The framework implies that a comprehensive XAI strategy should consider methods from all three phases to achieve robust and trustworthy interpretability. The inclusion of "By design" under Pre-modelling and "Model-based interpretable methods" under In-modelling highlights the value of building interpretability into the system, rather than relying solely on post-hoc analysis.

## Flowchart: Sequence-based Interpretation of XAI ### Overview The flowchart illustrates a structured process for interpreting Explainable Artificial Intelligence (XAI), divided into three phases: **Pre-modelling**, **In-modelling**, and **Post-modelling**. Each phase contains specific methods and components, with arrows indicating the flow of information and processes. --- ### Components/Axes - **Nodes**: Represent stages, methods, or concepts (e.g., "Data collection," "Surrogate model"). - **Arrows**: Indicate directional relationships between nodes. - **Color Coding**: - **Green**: Most nodes (standard processes). - **Orange**: Central node ("Sequence-based interpretation of XAI"). - **Red**: Highlighted central node (key focus). --- ### Detailed Analysis #### Pre-modelling 1. **Data collection**: Initial phase for gathering raw data. 2. **Data clarification**: Refining data for usability. 3. **By design**: Ensuring data aligns with predefined objectives. #### In-modelling 1. **Model-based interpretable methods**: Techniques to build inherently interpretable models. 2. **Attentional self-interpretation**: Combines prediction and explanation during model training. #### Post-modelling 1. **Visualisation**: - **Surrogate model**: Simplified model approximating complex ones. - **Control variable**: Isolating variables to test impact. - **Interactive method**: User-driven exploration of model behavior. 2. **Knowledge extraction**: - **Rule extraction**: Deriving decision rules from models. - **Model distillation**: Simplifying complex models into smaller ones. - **Compromise rule extraction**: Balancing accuracy and interpretability. 3. **Impact level methods**: - **Sensitivity analysis**: Testing how input changes affect outputs. - **Layered correlation communications**: Explaining relationships between variables. - **Characteristic importance**: Identifying key features influencing predictions. 4. **Instance-based samples**: - **Counterfactual explanations**: Hypothetical scenarios altering predictions. --- ### Key Observations - **Central Focus**: The orange "Sequence-based interpretation of XAI" node acts as the core, connecting all phases. - **Hierarchical Structure**: Pre-modelling sets the foundation, In-modelling integrates interpretability, and Post-modelling refines explanations. - **Color Emphasis**: The red central node draws attention to the overarching goal of sequence-based XAI interpretation. --- ### Interpretation The flowchart emphasizes a **systematic, iterative approach** to XAI, ensuring interpretability is embedded at every stage: 1. **Pre-modelling** ensures data quality and alignment with objectives. 2. **In-modelling** prioritizes transparency by integrating interpretability directly into model design (e.g., attentional self-interpretation). 3. **Post-modelling** focuses on post-hoc explanations, using visualization and rule extraction to make complex models understandable. Techniques like counterfactual explanations and sensitivity analysis highlight the model's behavior and robustness. This structure suggests that effective XAI requires **cross-phase collaboration**, where data preparation, model design, and post-hoc analysis work together to produce transparent, trustworthy AI systems.