# Applying Attribution Explanations in Truth-Discovery Quantitative Bipolar Argumentation Frameworks

arXiv: https://arxiv.org/abs/2409.05831 · HTML: https://mlsanity.com/paper/2409.05831/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 20 references that are in the map.
- First posted on arXiv 9 Sep 2024.

## Builds on (most cited first, 8 of 8)

- [Explaining Explanations in AI](https://mlsanity.com/paper/1811.01439/) (2018)
- [Argumentative XAI: A Survey](https://mlsanity.com/paper/2105.11266/) (2021)
- [Interpreting Neural Networks as Gradual Argumentation Frameworks (Including Proof Appendix)](https://mlsanity.com/paper/2012.05738/) (2020)
- [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)
- [Explaining Random Forests using Bipolar Argumentation and Markov Networks (Technical Report)](https://mlsanity.com/paper/2211.11699/) (2022)
- [Argument Attribution Explanations in Quantitative Bipolar Argumentation Frameworks (Technical Report)](https://mlsanity.com/paper/2307.13582/) (2023)
- [Explaining Arguments' Strength: Unveiling the Role of Attacks and Supports (Technical Report)](https://mlsanity.com/paper/2404.14304/) (2024)

## Built on by (most cited first, 1 of 1)

- [Contestability in Quantitative Argumentation](https://mlsanity.com/paper/2507.11323/) (2025)

## Abstract (arXiv)

Explaining the strength of arguments under gradual semantics is receiving increasing attention. For example, various studies in the literature offer explanations by computing the attribution scores of arguments or edges in Quantitative Bipolar Argumentation Frameworks (QBAFs). These explanations, known as Argument Attribution Explanations (AAEs) and Relation Attribution Explanations (RAEs), commonly employ removal-based and Shapley-based techniques for computing the attribution scores. While AAEs and RAEs have proven useful in several applications with acyclic QBAFs, they remain largely unexplored for cyclic QBAFs. Furthermore, existing applications tend to focus solely on either AAEs or RAEs, but do not compare them directly. In this paper, we apply both AAEs and RAEs, to Truth Discovery QBAFs (TD-QBAFs), which assess the trustworthiness of sources (e.g., websites) and their claims (e.g., the severity of a virus), and feature complex cycles. We find that both AAEs and RAEs can provide interesting explanations and can give non-trivial and surprising insights.