Verklarbare

Verklarbare is a Dutch adjective meaning capable of being explained or interpreted. In everyday use, it describes information, conclusions, processes, or systems whose reasoning can be understood by humans. In technical and policy contexts, the term is often attached to explanations of automated decision-making and data analysis.

In technology, verklarbare is frequently encountered in discussions of explainable artificial intelligence and explainable machine learning. These discussions distinguish between intrinsic interpretability, where models are designed to be understandable by design (for example, decision trees or sparse linear models), and post-hoc explainability, where explanations are generated after training for otherwise opaque models (such as neural networks) using methods like feature importance, SHAP, or LIME.

The emphasis on verklarbare systems reflects concerns about accountability, safety, and trust. Explanations must be faithful to the underlying processes, accessible to the intended audience, and helpful for decision-making. Debates often address the trade-off between interpretability and predictive performance, the quality of explanations, and the risk of oversimplification.

Beyond AI, verklarbare can describe transparent methodologies, clear data disclosures, or easy-to-understand results in research, journalism, and public policy. The term is used in academic literature, industry guidelines, and regulatory discussions to advocate for visibility into how conclusions are reached and decisions are made.

See also: explainable artificial intelligence, interpretable machine learning, model transparency, transparency-by-design.