Szczegóły publikacji
Opis bibliograficzny
In search for model-driven eXplainable Artificial Intelligence / Antoni LIGĘZA, Dominik SEPIOŁO // W: Artificial Intelligence for Knowledge Management, Energy and Sustainability : 10th IFIP International Workshop on Artificial Intelligence for Knowledge Management, AI4KMES 2023 : Krakow, Poland, September 30 – October 1, 2023 : revised selected papers / eds. Eunika Mercier-Laurent, [et al.]. — Cham : Springer Nature Switzerland, cop. 2024. — (IFIP Advances in Information and Communication Technology ; ISSN 1868-4238 ; vol. 693). — ISBN: 978-3-031-61068-4; e-ISBN: 978-3-031-61069-1. — S. 11–26. — Bibliogr. s. 25–26, Abstr.
Autorzy (2)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 153907 |
|---|---|
| Data dodania do BaDAP | 2024-06-26 |
| DOI | 10.1007/978-3-031-61069-1_2 |
| Rok publikacji | 2024 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Springer |
| Czasopismo/seria | IFIP Advances in Information and Communication Technology |
Abstract
This paper reports on ongoing and innovative research in the area of eXplainable Artificial Intelligence (XAI). A classical XAI task is considered as finding an explanation of the model generated via Machine Learning by identifying the most influential variables for local decision-making. Such an approach suffers from severe limitations. The proposed approach moves the explanatory process to a new, knowledge-level dimension. It is oriented towards Model Discovery, i.e. the internal structure and functions of the components. The concept of Model-Driven XAI is put forward and explained with examples. An experiment on Function Discovery via Grammatical Evolution is reported in brief.
