Medizinische Universität Graz - Research portal

Go directly to the nagivation.
Go directly to the content.

Navigation/Search

Selected Publication:

SHR Neuro Cancer Cardio Lipid Metab Microb

Pfeifer, B; Krzyzinski, M; Baniecki, H; Holzinger, A; Biecek, P.
Explaining and visualizing black-box models through counterfactual paths
PATTERN ANAL APPL. 2025; 28(3): 154 Doi: 10.1007/s10044-025-01532-8
Web of Science FullText FullText_MUG

Leading authors Med Uni Graz: Pfeifer Bastian
Co-authors Med Uni Graz: Holzinger Andreas
Altmetrics:
Dimensions Citations:
Plum Analytics:
Scite (citation analytics):
Abstract:: Explainable AI (XAI) is an increasingly important area of machine learning research, which aims to make black-box models transparent and interpretable. In this paper, we propose a novel approach to XAI that uses the so-called counterfactual paths for model-agnostic global explanations. The algorithm measures feature importance by identifying sequential permutations of features that most influence changes in model predictions. It is particularly suitable for generating explanations based on counterfactual paths in knowledge graphs incorporating domain knowledge. Counterfactual paths introduce an additional graph dimension to current XAI methods in both explaining and visualizing black-box models. Experiments with synthetic and bio-medical data demonstrate the practical applicability of our approach.
Find related publications in this database (Keywords): Explainable machine learning; Knowledge graph; Feature importance; Counterfactual explanation