Yin, Xiang, Potyka, Nico, Rago, Antonio, Kampik, Timotheus and Toni, Francesca
2026.
Contestability in edge-weighted quantitative bipolar argumentation frameworks.
Presented at: 23rd International Conference on Principles of Knowledge Representation and Reasoning (KR 2026),
Lisbon, Portugal,
July 20-23 2026.
Published in: Wassermann, R., Mugnier, M. and Baader, F. eds.
Proceedings of the 23rd International Conference on Principles of Knowledge Representation and Reasoning.
IJCAI,
pp. 676-687.
10.24963/kr.2026/64
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Abstract
Contestable AI requires that AI-driven decisions align with given preferences. Various types of argumentation frameworks have been shown to support forms of contestability. In this paper we focus on the little-studied Edge-Weighted Quantitative Bipolar Argumentation Frameworks (EW-QBAFs), where arguments have a base score as in QBAFs but attacks and supports (edges) are weighted. After generalising gradual semantics and properties thereof from QBAFs to EW-QBAFs, we introduce the contestability problem for EW-QBAFs, which asks how to modify edge weights to achieve a desired strength for a specific topic argument. To address this problem, we propose gradient-based relation attribution explanations (G-RAEs), which quantify the sensitivity of the topic argument's strength to changes in individual edge weights, thus providing interpretable guidance for weight adjustments towards contestability. Building on G-RAEs, we develop a heuristic algorithm that progressively adjusts the edge weights to attain the desired strength. We evaluate our approach experimentally on synthetic EW-QBAFs that simulate the structural characteristics of personalised recommender systems and multi-layer perceptrons, demonstrating that it can support contestability effectively.
| Item Type: | Conference or Workshop Item - published (Paper) |
|---|---|
| Date Type: | Publication |
| Status: | Published |
| Schools: | Schools > Computer Science & Informatics |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Additional Information: | RRS policy applied |
| Publisher: | IJCAI |
| ISBN: | 9781956792188 |
| Date of First Compliant Deposit: | 28 July 2026 |
| Last Modified: | 28 Jul 2026 14:15 |
| URI: | https://orca.cardiff.ac.uk/id/eprint/186634 |
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