Contestability in edge-weighted quantitative bipolar argumentation frameworks
File(s)
Accepted version
Author(s)
Yin, Xiang
Potyka, Nico
Rago, antonio
Kampik, Timotheus
Toni, francesca
Type
Conference Paper
Abstract
Contestable AI requires that AI-driven decisions align with
given preferences. Various types of argumentation frame-
works 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 GRAEs, 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.
given preferences. Various types of argumentation frame-
works 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 GRAEs, 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.
Date Acceptance
2026-04-13
Publisher
Knowledge Representation and Reasoning (KR)
Copyright Statement
Subject to copyright. This paper is embargoed until publication.
Source
Knowledge representation and Reasoning
Publication Status
Accepted
Start Date
2026-07-20
Finish Date
2026-07-23
Coverage Spatial
Lisbon, Portugal