explainabilitytechnieken
Explainability techniques, also known as interpretable AI or XAI, are methods and approaches used to understand and explain the predictions and decisions made by artificial intelligence models. In many applications, particularly those involving high-stakes decisions like medical diagnoses or loan approvals, it is crucial to not only know what a model predicts but also why it makes that prediction. This understanding builds trust, facilitates debugging, ensures fairness, and aids in regulatory compliance.
These techniques can broadly be categorized into two types: intrinsically interpretable models and post-hoc explainability methods.
Common post-hoc techniques include feature importance methods, which identify which input features had the most significant