A Guide to XAI Methods and Techniques

Why is a classification system needed for XAI methods?

A classification system, or taxonomy, is a practical necessity for Explainable AI (XAI). The field has a rapidly growing number of diverse methods and algorithms. A structured taxonomy acts like a conceptual map, organizing these methods along key dimensions. This helps researchers and practitioners navigate the complex landscape, compare different approaches, and choose the most appropriate tool for their specific needs.

What are the main ways to classify XAI methods? 🗺️

XAI methods are typically classified along several key dimensions, which describe their fundamental properties and how they work.

Intrinsic vs. Post-Hoc

This is the most basic distinction and relates to when the explanation is generated.

• Intrinsic: Refers to models that are "interpretable by design," like linear models or decision trees. The explanation is built into the model's structure, so no extra steps are needed to understand its logic.

• Post-Hoc: Refers to methods that are applied after a model has already been trained. They treat the model as a "black box" and probe it to generate an explanation. Most modern XAI techniques, like LIME and SHAP, fall into this category.

Model-Specific vs. Model-Agnostic

This describes how versatile a technique is across different types of AI models.

• Model-Specific: These techniques are designed for a particular class of models, often using their internal structure to create more efficient or accurate explanations. An example is using gradient-based methods, which only work on differentiable models like neural networks.

• Model-Agnostic: These techniques can be applied to any machine learning model, regardless of its architecture. They work by treating the model as a black box, interacting with it only through its inputs and outputs. This flexibility is a huge advantage.

Global vs. Local

This defines the scope of the explanation.

• Global: A global explanation aims to describe the overall behavior of the entire model. It answers questions like, "What are the most important features for this model on average?"

• Local: A local explanation focuses on explaining a single, individual prediction. It answers the question, "Why did the model make this specific prediction for this particular instance?"

What is the "glass-box" or "white-box" approach to explainability?

The "glass-box" or "white-box" approach is about building models that are transparent by design. Instead of trying to explain a complex model after the fact, this philosophy prioritizes using algorithms whose internal logic is inherently simple and easy for a human to inspect.

The core advantage is that the model itself serves as its own explanation. This eliminates the "fidelity problem," where a separate explanation might not accurately reflect the model's true reasoning. This approach is often used in highly regulated fields where complete transparency is a must.

What are some examples of intrinsically interpretable (white-box) models?

These models are simple enough that their decision-making process is clear.

• Linear Models: In linear and logistic regression, the learned coefficients (weights) for each feature provide a direct and quantifiable measure of its influence on the outcome.

• Decision Trees: A single decision tree creates a hierarchical set of IF-THEN rules. To understand a prediction, you can simply trace the clear, logical path from the tree's root to its final leaf node.

• Rule-Based Systems: These systems generate an explicit set of human-readable IF-THEN rules that make up the predictive model. Each rule can be examined and understood on its own.

• Generalized Additive Models (GAMs): GAMs are a flexible compromise between simple linear models and complex black-box methods. They model the outcome as a sum of smooth, non-linear functions of each feature. This allows them to capture complex relationships while still letting you see the impact of each feature independently.

What is the main trade-off when using white-box models?

The primary and persistent limitation of white-box models is the trade-off between interpretability and predictive performance.While they offer unmatched transparency, these simpler models often can't achieve the state-of-the-art accuracy of their black-box counterparts (like deep neural networks) on complex, high-dimensional data. This forces developers to make a critical choice: prioritize the inherent transparency of a simple model or pursue the higher accuracy of a complex model and rely on post-hoc methods for explanations.

What are post-hoc explanation techniques?

Post-hoc techniques are a diverse set of methods designed to explain complex "black-box" models. They are applied after a model has been trained and work by providing insights into its behavior without altering the model itself.Essentially, they create an "explainer" that acts as a translator, sitting between the complex model and the human user to make the model's logic understandable.

What is the main risk when using a post-hoc approach?

When you use an intrinsically interpretable model, the main risk is that it might not be accurate enough. When you use a post-hoc approach with a black-box model, the main risk shifts to explanation fidelity.

The critical question becomes: how can we be sure that the explanation generated by a tool like LIME is a true and faithful representation of what the complex neural network is actually doing? This creates a "meta-problem" of validating the explainer itself. An unfaithful explanation can be dangerously misleading.

This strategic choice—between performance risk and explanation fidelity risk—must be made consciously at the start of a project.

What are the major families of post-hoc XAI methods?

The vast landscape of post-hoc methods can be organized into several major families:

1. Surrogate Models: This approach involves training a simple, interpretable model (the "surrogate") to mimic the predictions of the complex black-box model. LIME is a famous example of a local surrogate model.

2. Feature Attribution/Importance: These methods assign a score to each input feature, quantifying its contribution to a specific prediction. Popular techniques include SHAP and Integrated Gradients.

3. Example-Based Explanations: These methods use specific data points to explain a model's behavior. This family includes Counterfactual Explanations (showing what needs to change to alter a prediction) and Anchors (finding rule-based conditions).

4. Visualization-Based Explanations: These methods use visual aids, like heatmaps (saliency maps), to highlight the parts of an input (like an image or text) that the model focused on.

What is the "challenge of fidelity" in post-hoc XAI?

The challenge of fidelity, also called explanation accuracy, is a critical and recurring theme for all post-hoc methods.Because a post-hoc explanation is an approximation of the original model's behavior, there is always a risk that it is an inaccurate or misleading one. An unfaithful explanation can be worse than no explanation at all, as it can create a false sense of understanding and lead to misplaced trust in the AI system. Evaluating and ensuring the fidelity of explanations is one of the biggest open challenges in the XAI field.