Szczegóły publikacji
Opis bibliograficzny
Comparing explanations from glass-box and black-box machine-learning models / Michał KUK, Szymon Bobek, Grzegorz J. Nalepa // W: Computational Science – ICCS 2022 : 22nd international conference : London, UK, June 21–23, 2022 : proceedings, Pt. 3 / eds. Derek Groen, Clélia de Mulatier, Maciej Paszyński, Valeria V. Krzhizhanovskaya, Jack J. Dongarra, Peter M. A. Sloot. — Cham : Springer Nature Switzerland, cop. 2022. — (Lecture Notes in Computer Science ; ISSN 0302-9743 ; LNCS 13352). — ISBN: 978-3-031-08756-1; e-ISBN: 978-3-031-08757-8. — S. 668–675. — Bibliogr., Abstr. — Publikacja dostępna online od: 2022-06-15
Autorzy (3)
- AGHKuk Michał
- Bobek Szymon
- Nalepa Grzegorz
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 140686 |
|---|---|
| Data dodania do BaDAP | 2022-06-27 |
| DOI | 10.1007/978-3-031-08757-8_55 |
| Rok publikacji | 2022 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Springer |
| Konferencja | 22nd International Conference on Computational Science |
| Czasopismo/seria | Lecture Notes in Computer Science |
Abstract
Explainable Artificial Intelligence (XAI) aims at introducing transparency and intelligibility into the decision-making process of AI systems. In recent years, most efforts were made to build XAI algorithms that are able to explain black-box models. However, in many cases, including medical and industrial applications, the explanation of a decision may be worth equally or even more than the decision itself. This imposes a question about the quality of explanations. In this work, we aim at investigating how the explanations derived from black-box models combined with XAI algorithms differ from those obtained from inherently interpretable glass-box models. We also aim at answering the question whether there are justified cases to use less accurate glass-box models instead of complex black-box approaches. We perform our study on publicly available datasets.