interpretierbar

Interpretierbar describes the ability of a model, analysis, or result to be read, understood, and reasoned about by humans. In scientific and engineering contexts, something interpretierbar is transparent enough that its inputs, processes, and outputs can be traced and explained. The term is often used in the German discourse as a counterpart to less transparent, “black-box” approaches. It is related to, but not identical with, the concept of Erklärbarkeit (explainability); interpretierbarkeit emphasizes human comprehensibility of how a model behaves, while Erklärbarkeit centers on producing understandable explanations for particular decisions or outcomes.

In data science and machine learning, interpretierbare Modelle include linear regression, decision trees, and other inherently transparent methods. For more complex models—such as ensembles or deep neural networks—interpretierbarkeit is often pursued with post hoc explanation techniques that reveal feature contributions or global behavior. Common approaches include feature importance, partial dependence plots, SHAP values, and LIME. The aim is to provide user-friendly rationales for predictions or for the model’s overall logic without disclosing every internal parameter.

Practically, interpretierbarkeit supports trust, governance, debugging, and compliance, especially in high-stakes domains like finance, healthcare, and the public sector. It involves trade-offs between interpretability and predictive accuracy, concerns about the reliability of explanations, and risks of oversimplification. Ongoing research explores inherently interpretable model classes and standardized methods for evaluating interpretability, with emphasis on responsible use and clear communication of limitations.