# Interactive Explanations by Conflict Resolution via Argumentative Exchanges

arXiv: https://arxiv.org/abs/2303.15022 · HTML: https://mlsanity.com/paper/2303.15022/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 57 references that are in the map.
- First posted on arXiv 27 Mar 2023.

## Builds on (most cited first, 16 of 16)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR](https://mlsanity.com/paper/1711.00399/) (2017)
- [A Survey of Human-in-the-loop for Machine Learning](https://mlsanity.com/paper/2108.00941/) (2021)
- [Abduction-Based Explanations for Machine Learning Models](https://mlsanity.com/paper/1811.10656/) (2018)
- [A Symbolic Approach to Explaining Bayesian Network Classifiers](https://mlsanity.com/paper/1805.03364/) (2018)
- [Explainability Fact Sheets: A Framework for Systematic Assessment of Explainable Approaches](https://mlsanity.com/paper/1912.05100/) (2019)
- [Argumentative XAI: A Survey](https://mlsanity.com/paper/2105.11266/) (2021)
- [Leveraging Explanations in Interactive Machine Learning: An Overview](https://mlsanity.com/paper/2207.14526/) (2022)
- [Rethinking Explainability as a Dialogue: A Practitioner's Perspective](https://mlsanity.com/paper/2202.01875/) (2022)
- [FIND: Human-in-the-Loop Debugging Deep Text Classifiers](https://mlsanity.com/paper/2010.04987/) (2020)
- [Interpreting Neural Networks as Gradual Argumentation Frameworks (Including Proof Appendix)](https://mlsanity.com/paper/2012.05738/) (2020)
- [Culture-Based Explainable Human-Agent Deconfliction](https://mlsanity.com/paper/1911.10098/) (2019)
- [Machine Learning for Utility Prediction in Argument-Based Computational Persuasion](https://mlsanity.com/paper/2112.04953/) (2021)
- [On Interactive Explanations as Non-Monotonic Reasoning](https://mlsanity.com/paper/2208.00316/) (2022)
- [Explaining Causal Models with Argumentation: the Case of Bi-variate Reinforcement](https://mlsanity.com/paper/2205.11589/) (2022)

## Built on by (most cited first, 24 of 24)

- [Argumentative Large Language Models for Explainable and Contestable Claim Verification](https://mlsanity.com/paper/2405.02079/) (2024)
- [Contestable AI needs Computational Argumentation](https://mlsanity.com/paper/2405.10729/) (2024)
- [Retrieval- and Argumentation-Enhanced Multi-Agent LLMs for Judgmental Forecasting (Extended Version with Supplementary Material)](https://mlsanity.com/paper/2510.24303/) (2025)
- [Dialectical Reconciliation via Structured Argumentative Dialogues](https://mlsanity.com/paper/2306.14694/) (2023)
- ["My Grade is Wrong!": A Contestable AI Framework for Interactive Feedback in Evaluating Student Essays](https://mlsanity.com/paper/2409.07453/) (2024)
- [CE-QArg: Counterfactual Explanations for Quantitative Bipolar Argumentation Frameworks (Technical Report)](https://mlsanity.com/paper/2407.08497/) (2024)
- [A Methodology for Incompleteness-Tolerant and Modular Gradual Semantics for Argumentative Statement Graphs](https://mlsanity.com/paper/2410.22209/) (2024)
- [Free Argumentative Exchanges for Explaining Image Classifiers](https://mlsanity.com/paper/2502.12995/) (2025)
- [Does Your AI Agent Get You? A Personalizable Framework for Approximating Human Models from Argumentation-based Dialogue Traces](https://mlsanity.com/paper/2502.16376/) (2025)
- [Contestability in Quantitative Argumentation](https://mlsanity.com/paper/2507.11323/) (2025)
- [Explainable Interface for Human-Autonomy Teaming: A Survey](https://mlsanity.com/paper/2405.02583/) (2024)
- [KRAG Framework for Enhancing LLMs in the Legal Domain](https://mlsanity.com/paper/2410.07551/) (2024)
- [Argumentatively Coherent Judgmental Forecasting](https://mlsanity.com/paper/2507.23163/) (2025)
- [On Generating Monolithic and Model Reconciling Explanations in Probabilistic Scenarios](https://mlsanity.com/paper/2405.19229/) (2024)
- [From User Preferences to Base Score Extraction Functions in Gradual Argumentation (with Appendix)](https://mlsanity.com/paper/2602.14674/) (2026)
- [Evaluating LLM-Driven Summarisation of Parliamentary Debates with Computational Argumentation](https://mlsanity.com/paper/2604.19331/) (2026)
- [Towards an Argumentative Foundation for Evaluative AI](https://mlsanity.com/paper/2608.07473/) (2026)
- [Approximating Human Models During Argumentation-based Dialogues](https://mlsanity.com/paper/2405.18650/) (2024)
- [Temporal Many-valued Conditional Logics: a Preliminary Report](https://mlsanity.com/paper/2409.09069/) (2024)
- [Multi-User Personalisation in Human-Robot Interaction: Resolving Preference Conflicts Using Gradual Argumentation](https://mlsanity.com/paper/2511.03576/) (2025)
- [Adaptive Arena-based Contestable Argumentative Network-of-Experts for Open-Ended Care Plan Coordination](https://mlsanity.com/paper/2608.05391/) (2026)
- [A Theory of Post-hoc Debate Judgement](https://mlsanity.com/paper/2608.19002/) (2026)
- [Self-Evolving Multimedia Verification through Memory Consolidation of Contestation Experiences](https://mlsanity.com/paper/2609.27175/) (2026)
- [OmouAI: Argumentative Human-AI Policy Deliberation with Simulated Personas](https://mlsanity.com/paper/2609.31078/) (2026)

## Abstract (arXiv)

As the field of explainable AI (XAI) is maturing, calls for interactive explanations for (the outputs of) AI models are growing, but the state-of-the-art predominantly focuses on static explanations. In this paper, we focus instead on interactive explanations framed as conflict resolution between agents (i.e. AI models and/or humans) by leveraging on computational argumentation. Specifically, we define Argumentative eXchanges (AXs) for dynamically sharing, in multi-agent systems, information harboured in individual agents' quantitative bipolar argumentation frameworks towards resolving conflicts amongst the agents. We then deploy AXs in the XAI setting in which a machine and a human interact about the machine's predictions. We identify and assess several theoretical properties characterising AXs that are suitable for XAI. Finally, we instantiate AXs for XAI by defining various agent behaviours, e.g. capturing counterfactual patterns of reasoning in machines and highlighting the effects of cognitive biases in humans. We show experimentally (in a simulated environment) the comparative advantages of these behaviours in terms of conflict resolution, and show that the strongest argument may not always be the most effective.