Vysvetlitelnost

Vysvetlitelnost refers to the degree to which a human can understand the cause of a decision made by a machine learning model. In simpler terms, it's about being able to explain why a model produced a particular output. This is crucial in many applications, especially those with high stakes, such as medical diagnoses, financial lending, or autonomous driving. Without vysvetlitelnost, it becomes difficult to trust, debug, or improve these complex systems.

There are various approaches to achieving vysvetlitelnost. Some models are inherently interpretable, meaning their internal workings are relatively easy to understand. Examples include simple linear regression or decision trees. Other more complex models, like deep neural networks, are often considered "black boxes" because their decision-making processes are very intricate. For these models, techniques like LIME (Local Interpretable Model-agnostic Explanations) or SHAP (SHapley Additive exPlanations) are used to provide local explanations for individual predictions.

The importance of vysvetlitelnost is growing as AI systems become more integrated into our lives. It facilitates accountability, enabling us to understand who or what is responsible when something goes wrong. It also aids in detecting and mitigating biases that might be present in the data or the model itself. Furthermore, understanding model behavior can lead to new insights and discoveries in the domain where the model is applied. Regulations in some sectors are also beginning to mandate a certain level of vysvetlitelnost for AI systems.