erklärungsbasierte

Erklärungsbasierte refers to a German term that translates to "explanation-based" in English. It describes approaches, systems, or methodologies that prioritize providing clear and understandable explanations for their reasoning or decisions. This concept is particularly relevant in fields like artificial intelligence, machine learning, and software development, where transparency and user comprehension are crucial.

In the context of AI and machine learning, an erklärungsbasierte system aims not only to achieve a desired outcome but also to articulate *how* it arrived at that outcome. This involves generating justifications, justifications, or step-by-step breakdowns of the decision-making process. Such explanations can help users trust the system's outputs, identify potential errors or biases, and gain insights into the underlying logic.

The development of erklärungsbasierte systems often involves techniques that generate human-readable descriptions of model behavior, identify key features influencing a prediction, or visualize the internal workings of an algorithm. The goal is to move beyond "black box" models, where the internal processes are opaque, towards more transparent and interpretable artificial intelligence. This is essential for applications where accountability, safety, and user education are paramount, such as in healthcare, finance, or autonomous systems.