Szczegóły publikacji
Opis bibliograficzny
Towards model-driven explainable artificial intelligence : an experiment with shallow methods versus grammatical evolution / Dominik SEPIOŁO, Antoni LIGĘZA // W: Artificial intelligence : ECAI 2023 international workshop : XAI${^{\wedge}}$3, TACTIFUL, XI-ML, SEDAMI, RAAIT, AI4S, HYDRA, AI4AI : Kraków, Poland, September 30 – October 4, 2023 : proceedings, Pt. 2 / eds. Sławomir Nowaczyk, Przemysław Biecek, Neo Christopher Chung, Mauro Vallati, Paweł Skruch, Joanna Jaworek-Korjakowska, Simon Parkinson, Alexandros Nikitas, Martin Atzmüller, Tomáš Kliegr, Ute Schmid, Szymon Bobek, Nada Lavrac, Marieke Peeters, Roland van Dierendonck, Saskia Robben, Eunika Mercier-Laurent, Gülgün Kayakutlu, Mieczysław Lech Owoc, Karl Mason, Abdul Wahid, Pierangela Bruno, Francesco Calimeri, Francesco Cauteruccio, Giorgio Terracina, Diedrich Wolter, Jochen L. Leidner, Michael Kohlhase, Vania Dimitrova. — Cham : Springer Nature Switzerland, cop. 2024. — (Communications in Computer and Information Science ; ISSN 1865-0929 ; CCIS 1948). — ISBN: 978-3-031-50484-6; e-ISBN: 978-3-031-50485-3. — S. 360–365. — Bibliogr., Abstr. — Publikacja dostępna online od: 2024-01-25
Autorzy (2)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 151695 |
|---|---|
| Data dodania do BaDAP | 2024-03-13 |
| DOI | 10.1007/978-3-031-50485-3_36 |
| Rok publikacji | 2024 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Springer |
| Konferencja | 26th European Conference on Artificial Intelligence |
| Czasopismo/seria | Communications in Computer and Information Science |
Abstract
This paper reports on ongoing and innovative research in the area of eXplainable Artificial Intelligence (XAI). An 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. The proposed approach moves the explanatory process to a new, deeper-level dimension. It is oriented towards Model Discovery, i.e. the internal structure and functions of the components. An experiment on Function Discovery via Grammatical Evolution is reported in brief.