tõlgendatavusele

Tõlgendatavus, meaning interpretability in English, 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 understanding why a model arrived at a particular output. This is crucial in many fields, especially when high stakes are involved, such as medical diagnosis, financial lending, or autonomous driving.

There are broadly two categories of interpretability. Model-specific interpretability focuses on understanding a particular type of model, like decision trees or linear regression, which are inherently interpretable due to their transparent structure. Model-agnostic interpretability, on the other hand, applies to any model, regardless of its complexity. Techniques like LIME (Local Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive exPlanations) fall into this category, aiming to explain individual predictions by approximating the complex model with simpler, understandable ones.

The importance of tõlgendatavus stems from several factors. It builds trust in AI systems, allowing users to verify that the model is making decisions based on relevant factors and not on spurious correlations or biases. It aids in debugging and improving models by identifying flaws or unexpected behavior. Furthermore, regulatory requirements in certain industries often mandate explainable AI, making tõlgendatavus a legal and ethical necessity. As AI systems become more pervasive, the demand for interpretable models will only continue to grow.