Modellerklärungen

Modellerklärungen, often translated as model explanations or model interpretability, refers to the process of understanding how a machine learning model arrives at its predictions. This field is crucial for building trust and ensuring the responsible deployment of AI systems. Without clear explanations, it can be difficult to diagnose errors, identify biases, or satisfy regulatory requirements.

There are various approaches to achieving model explanations. Model-agnostic methods work with any machine learning model, treating it as a black box. Techniques like LIME (Local Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive exPlanations) fall into this category. LIME explains individual predictions by approximating the complex model with a simpler, interpretable model in the local vicinity of the prediction. SHAP, on the other hand, uses game theory to attribute the contribution of each feature to the model's output.

Model-specific methods are tailored to particular model architectures. For instance, decision trees are inherently interpretable due to their hierarchical structure. Linear models offer explanations through their coefficients, indicating the direction and magnitude of feature influence. Deep learning models, being highly complex, often require more sophisticated techniques, such as attention mechanisms or saliency maps, to visualize which parts of the input are most important for a given prediction. The choice of explanation method depends on the model, the data, and the specific goals of the user.