Abstract:Explainable AI (XAI) methods identify which features are relevant to a model's predictions but often fail to clarify why certain decisions are made. In this work, we present a novel method that integrates causality with argument-based reasoning to explain why models may be making predictions. Our approach first identifies causal relationships among variables using causal discovery methods and then translates these into a Bipolar Argumentation Framework (BAF) to represent supportive and opposing interactions among features. By using semi-stable semantics, we find extensions of features that explain why certain outcomes may have been chosen. We demonstrate our method on two benchmark datasets and compare its results against standard post-hoc explainability approaches.
| Comments: | To be published in The 4th World Conference on eXplainable Artificial Intelligence |
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2605.21758 [cs.AI] |
| (or arXiv:2605.21758v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2605.21758 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Henry Salgado [view email]
[v1]
Wed, 20 May 2026 21:40:44 UTC (1,965 KB)