Értelemezhetség

Értelemezhetség is a Hungarian term that translates roughly to "interpretability" or "explainability" in English. It refers to the degree to which the inner workings of a system, particularly a machine learning model, can be understood by a human. This concept is crucial in various fields, especially where decisions made by artificial intelligence have significant consequences, such as in healthcare, finance, or autonomous driving.

A highly interpretable model allows users to understand why it makes a particular prediction or decision. This can involve identifying the specific features or data points that influenced the outcome and understanding the relationships between these features and the output. Conversely, a model with low értelemezhetség is often considered a "black box," where the reasoning behind its decisions is opaque and difficult to discern.

The importance of értelemezhetség stems from several factors. Firstly, it builds trust in AI systems. When users can understand how a model works, they are more likely to rely on its outputs. Secondly, it facilitates debugging and improvement. If a model produces incorrect results, understanding its internal logic can help developers identify and fix the underlying issues. Thirdly, it is essential for ethical considerations and regulatory compliance. In many domains, it is a legal or ethical requirement to be able to explain the basis for decisions that affect individuals.

While complex models like deep neural networks often exhibit low értelemezhetség, research is ongoing to develop methods that enhance their explainability, sometimes referred to as "explainable AI" or XAI. These methods aim to strike a balance between model performance and human comprehensibility.