# Contestability in Quantitative Argumentation

arXiv: https://arxiv.org/abs/2507.11323 · HTML: https://mlsanity.com/paper/2507.11323/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 28 references that are in the map.
- First posted on arXiv 15 Jul 2025.

## Builds on (most cited first, 9 of 9)

- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Argumentative Large Language Models for Explainable and Contestable Claim Verification](https://mlsanity.com/paper/2405.02079/) (2024)
- [Interactive Explanations by Conflict Resolution via Argumentative Exchanges](https://mlsanity.com/paper/2303.15022/) (2023)
- [Modular Semantics and Characteristics for Bipolar Weighted Argumentation Graphs](https://mlsanity.com/paper/1807.06685/) (2018)
- [Contribution Functions for Quantitative Bipolar Argumentation Graphs: A Principle-based Analysis](https://mlsanity.com/paper/2401.08879/) (2024)
- [Perturbation Analysis of Gradient-based Adversarial Attacks](https://mlsanity.com/paper/2006.01456/) (2020)
- [Causal Discovery and Knowledge Injection for Contestable Neural Networks (with Appendices)](https://mlsanity.com/paper/2205.09787/) (2022)
- [Towards a Theory of Faithfulness: Faithful Explanations of Differentiable Classifiers over Continuous Data](https://mlsanity.com/paper/2205.09620/) (2022)
- [Applying Attribution Explanations in Truth-Discovery Quantitative Bipolar Argumentation Frameworks](https://mlsanity.com/paper/2409.05831/) (2024)

## Built on by (most cited first, 3 of 3)

- [Argumentation for Explainable and Globally Contestable Decision Support with LLMs](https://mlsanity.com/paper/2603.14643/) (2026)
- [From User Preferences to Base Score Extraction Functions in Gradual Argumentation (with Appendix)](https://mlsanity.com/paper/2602.14674/) (2026)
- [Multi-User Personalisation in Human-Robot Interaction: Resolving Preference Conflicts Using Gradual Argumentation](https://mlsanity.com/paper/2511.03576/) (2025)

## Abstract (arXiv)

Contestable AI requires that AI-driven decisions align with human preferences. While various forms of argumentation have been shown to support contestability, Edge-Weighted Quantitative Bipolar Argumentation Frameworks (EW-QBAFs) have received little attention. In this work, we show how EW-QBAFs can be deployed for this purpose. Specifically, we introduce the contestability problem for EW-QBAFs, which asks how to modify edge weights (e.g., preferences) to achieve a desired strength for a specific argument of interest (i.e., a 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 an iterative 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, and demonstrate that it can solve the problem effectively.