On the robustness of argumentative explanations - Rapberger,A; Toni,F (IOS Press, Inc., 2024)
The field of explainable AI has grown exponentially in recent years.Within this landscape, argumentation frameworks have shown to be helpful ab-stractions of some AI models towards providing explanations thereof. While exist-ing work on argumentative explanations and their properties has focused on staticsettings, we focus on dynamic settings whereby the (AI models underpinning the)argumentation frameworks need to change. Specifically, for a number of notionsof explanations drawn from abstract argumentation frameworks under extension-based semantics, we address the following questions: (1) Are explanations robust toextension-preserving changes, in the sense that they are still valid when the changesdo not modify the extensions? (2) If not, are these explanations pseudo-robust inthat can be tractably updated? In this paper, we frame these questions formally. Weconsider robustness and pseudo-robustness w.r.t. ordinary and strong equivalenceand provide several results for various extension-based semantics.