|
|
|
|
|
Parisa Mahya and Johannes Fürnkranz
Recently, some effort went into explaining intransparent and black-box models, such as deep neural networks or random forests. So-called model-agnostic methods typically approximate the prediction of the intransparent black-box model with an interpretabl...
ver más
|
|
|
|
|
|
|
|
|
|
|
Ezekiel Bernardo and Rosemary Seva
Explainable Artificial Intelligence (XAI) has successfully solved the black box paradox of Artificial Intelligence (AI). By providing human-level insights on AI, it allowed users to understand its inner workings even with limited knowledge of the machine...
ver más
|
|
|
|
|
|
|
|
|
|
|
Lev Utkin and Andrei Konstantinov
The ensemble-based modifications of the well-known SHapley Additive exPlanations (SHAP) method for the local explanation of a black-box model are proposed. The modifications aim to simplify the SHAP which is computationally expensive when there is a larg...
ver más
|
|
|
|
|
|