Benchmarking and survey of explanation methods for black box models

• Published in: Data mining and knowledge discovery Vol. 37; no. 5; pp. 1719 - 1778
• Main Authors: Bodria, Francesco, Giannotti, Fosca, Guidotti, Riccardo, Naretto, Francesca, Pedreschi, Dino, Rinzivillo, Salvatore
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.09.2023; Springer Nature B.V
• Subjects: Academic Surveys and Tutorials; Art exhibits; Artificial Intelligence; Black boxes; Chemistry and Earth Sciences; Computer Science; Data Mining and Knowledge Discovery; Decision making; Ethics; Information Storage and Retrieval; Machine learning; Mathematical models; Physics; Special Issue on Explainable and Interpretable Machine Learning and Data Mining; Statistics for Engineering; Trust; Explainable artificial intelligence; Interpretable machine learning; Benchmarking; Transparent models

The rise of sophisticated black-box machine learning models in Artificial Intelligence systems has prompted the need for explanation methods that reveal how these models work in an understandable way to users and decision makers. Unsurprisingly, the state-of-the-art exhibits currently a plethora of...