értelmezhetsége

Értelmezhetősége is a Hungarian term that translates to "interpretability" or "explainability" in English. It refers to the degree to which the workings of a model or system can be understood by humans. In the context of artificial intelligence and machine learning, értelmezhetősége is a crucial concept, particularly for complex models like deep neural networks, which are often considered "black boxes."

High értelmezhetősége means that it is easy to understand why a model makes a particular prediction or decision. This can involve identifying the features that most strongly influence the outcome, understanding the relationships between these features, and tracing the flow of information within the model. Conversely, low értelmezhetősége implies that the model's internal logic is opaque and difficult for humans to comprehend.

The importance of értelmezhetősége stems from several factors. In critical applications such as healthcare or finance, understanding the reasoning behind a model's output is essential for trust, accountability, and debugging. If a model makes an incorrect diagnosis or a flawed financial recommendation, knowing why it did so is vital for rectifying the error and preventing future mistakes. Furthermore, regulatory requirements in various sectors increasingly demand transparency and explainability in automated decision-making systems. Researchers are actively developing methods and techniques to enhance the értelmezhetősége of AI models, aiming to bridge the gap between powerful predictive capabilities and human comprehension.