Magyarázottság

Magyarázottság is a Hungarian term that translates to "explainability" or "interpretability" in English, particularly in the context of artificial intelligence and machine learning. It refers to the degree to which the inner workings of an AI model can be understood by humans. A highly magyarázottság model is one whose decision-making process is transparent and can be easily followed and comprehended. Conversely, a low magyarázottság model, often referred to as a "black box," makes predictions or decisions without revealing how it arrived at those conclusions.

The importance of magyarázottság has grown significantly as AI systems become more integrated into critical applications, such as healthcare, finance, and autonomous driving. In these fields, understanding why a model made a particular recommendation or took a specific action is crucial for trust, accountability, and debugging. If an AI system makes an error, its explainability allows developers to identify the cause and rectify it. Furthermore, regulatory requirements in certain sectors may necessitate a certain level of transparency in AI decision-making.

Research in magyarázottság focuses on developing techniques and tools to make complex AI models more interpretable. This includes methods like feature importance analysis, which highlights which input features contributed most to a model's output, and surrogate models, which are simpler, interpretable models trained to mimic the behavior of a more complex black-box model. The goal is to strike a balance between model performance and human understandability, ensuring that AI systems are not only effective but also trustworthy and accountable.