# Argument Attribution Explanations in Quantitative Bipolar Argumentation Frameworks (Technical Report)

arXiv: https://arxiv.org/abs/2307.13582 · HTML: https://mlsanity.com/paper/2307.13582/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 53 references that are in the map.
- First posted on arXiv 25 Jul 2023.

## Builds on (most cited first, 12 of 12)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Learning Important Features Through Propagating Activation Differences](https://mlsanity.com/paper/1704.02685/) (2017)
- [Not Just a Black Box: Learning Important Features Through Propagating Activation Differences](https://mlsanity.com/paper/1605.01713/) (2016)
- [Explaining by Removing: A Unified Framework for Model Explanation](https://mlsanity.com/paper/2011.14878/) (2020)
- [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)
- [What Makes a Good Explanation?: A Harmonized View of Properties of Explanations](https://mlsanity.com/paper/2211.05667/) (2022)
- [Attacker and Defender Counting Approach for Abstract Argumentation](https://mlsanity.com/paper/1506.04272/) (2015)
- [Deep Argumentative Explanations](https://mlsanity.com/paper/2012.05766/) (2020)
- [Towards a Theory of Faithfulness: Faithful Explanations of Differentiable Classifiers over Continuous Data](https://mlsanity.com/paper/2205.09620/) (2022)

## Built on by (most cited first, 7 of 7)

- [Contribution Functions for Quantitative Bipolar Argumentation Graphs: A Principle-based Analysis](https://mlsanity.com/paper/2401.08879/) (2024)
- [Impact Measures for Gradual Argumentation Semantics](https://mlsanity.com/paper/2407.08302/) (2024)
- [Adaptive Collaboration of Arena-Based Argumentative LLMs for Explainable and Contestable Legal Reasoning](https://mlsanity.com/paper/2602.18916/) (2026)
- [Explaining Arguments' Strength: Unveiling the Role of Attacks and Supports (Technical Report)](https://mlsanity.com/paper/2404.14304/) (2024)
- [A Methodology for Incompleteness-Tolerant and Modular Gradual Semantics for Argumentative Statement Graphs](https://mlsanity.com/paper/2410.22209/) (2024)
- [Set Contribution Functions for Quantitative Bipolar Argumentation and their Principles](https://mlsanity.com/paper/2509.14963/) (2025)
- [Applying Attribution Explanations in Truth-Discovery Quantitative Bipolar Argumentation Frameworks](https://mlsanity.com/paper/2409.05831/) (2024)

## Abstract (arXiv)

Argumentative explainable AI has been advocated by several in recent years, with an increasing interest on explaining the reasoning outcomes of Argumentation Frameworks (AFs). While there is a considerable body of research on qualitatively explaining the reasoning outcomes of AFs with debates/disputes/dialogues in the spirit of extension-based semantics, explaining the quantitative reasoning outcomes of AFs under gradual semantics has not received much attention, despite widespread use in applications. In this paper, we contribute to filling this gap by proposing a novel theory of Argument Attribution Explanations (AAEs) by incorporating the spirit of feature attribution from machine learning in the context of Quantitative Bipolar Argumentation Frameworks (QBAFs): whereas feature attribution is used to determine the influence of features towards outputs of machine learning models, AAEs are used to determine the influence of arguments towards topic arguments of interest. We study desirable properties of AAEs, including some new ones and some partially adapted from the literature to our setting. To demonstrate the applicability of our AAEs in practice, we conclude by carrying out two case studies in the scenarios of fake news detection and movie recommender systems.