Szczegóły publikacji
Opis bibliograficzny
Towards model-agnostic ensemble explanations / Szymon BOBEK, Paweł Bałaga, Grzegorz J. NALEPA // W: Computational Science – ICCS 2021 : 21st international conference : Krakow, Poland, June 16–18, 2021 : proceedings, Pt. 4 / eds. Maciej Paszyński, [et al.]. — Cham : Springer Nature Switzerland, cop. 2021. — (Lecture Notes in Computer Science ; ISSN 0302-9743 ; LNCS 12745. Theoretical Computer Science and General Issues ; ISSN 0302-9743). — ISBN: 978-3-030-77969-6; e-ISBN: 978-3-030-77970-2. — S. 39–51. — Bibliogr., Abstr. — Publikacja dostępna online od: 2021-06-09. — Sz. Bobek, G. J. Nalepa - pierwsza afiliacja: Jagiellonian University
Autorzy (3)
- AGHBobek Szymon
- Bałaga Paweł
- AGHNalepa Grzegorz Jacek
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 134730 |
|---|---|
| Data dodania do BaDAP | 2021-06-24 |
| DOI | 10.1007/978-3-030-77970-2_4 |
| Rok publikacji | 2021 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Springer |
| Konferencja | International Conference on Computational Science 2021 |
| Czasopisma/serie | Lecture Notes in Computer Science, Theoretical Computer Science and General Issues |
Abstract
Explainable Artificial Intelligence (XAI) methods form a large portfolio of different frameworks and algorithms. Although the main goal of all of explanation methods is to provide an insight into the decision process of AI system, their underlying mechanisms may differ. This may result in very different explanations for the same tasks. In this work, we present an approach that aims at combining several XAI algorithms into one ensemble explanation mechanism via quantitative, automated evaluation framework. We focus on model-agnostic explainers to provide most robustness and we demonstrate our approach on image classification task.
