interpretálhatóság

Interpretálhatóság refers to the degree to which a human can understand the cause of a decision made by an artificial intelligence system. In simpler terms, it's about understanding why an AI arrived at a particular conclusion or prediction. This is crucial because many AI models, especially complex ones like deep neural networks, can be opaque, meaning their internal workings are difficult to decipher.

The importance of interpretability stems from several factors. Firstly, it builds trust in AI systems. If users can understand how an AI makes decisions, they are more likely to rely on its outputs, especially in critical applications like healthcare or finance. Secondly, interpretability aids in debugging and improving AI models. By understanding the reasons behind incorrect predictions, developers can identify flaws in the data or the model's architecture and make necessary adjustments. Thirdly, it is often a legal and ethical requirement. Regulations in various sectors may mandate that decisions affecting individuals are explainable.

There are different levels and types of interpretability. Some models are inherently interpretable due to their simple structure, like linear regression or decision trees. For more complex models, techniques like LIME (Local Interpretable Model-agnostic Explanations) or SHAP (SHapley Additive exPlanations) are used to provide explanations for individual predictions. These methods aim to approximate the complex model's behavior with simpler, understandable ones, at least locally around a specific data point. The ongoing research in this field seeks to strike a balance between model performance and its comprehensibility.