Tulkinnanläpinäkyvyys

Tulkinnanläpinäkyvyys, also known as interpretability or explainability, 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 is about how well we can comprehend why a model arrived at a particular conclusion. This concept is crucial in various fields, especially where decisions have significant consequences, such as in healthcare, finance, and autonomous systems.

High tulkinnanläpinäkyvyys means that the internal workings of the model are easily understood, allowing users to trace the reasoning behind its predictions. This can involve identifying which input features were most influential in the decision-making process. Conversely, models with low tulkinnanläpinäkyvyys, often referred to as "black boxes," are difficult to interpret, making it challenging to ascertain the rationale behind their outputs.

The importance of tulkinnanläpinäkyvyys stems from several factors. It builds trust in AI systems, as users are more likely to rely on models they can understand. It also facilitates debugging and model improvement, as identifying the source of errors or biases becomes easier. Furthermore, in regulated industries, explainability is often a legal or ethical requirement to ensure fairness and accountability. Research in this area focuses on developing techniques and models that balance predictive performance with interpretability, aiming to make complex AI systems more transparent and trustworthy for human users.