---
title: "Counterfactual Explanations Under Model Multiplicity and Their Use in Computational Argumentation"
authors:
  - "Gianvincenzo Alfano"
  - "Adam Gould"
  - "Francesco Leofante"
  - "Antonio Rago"
  - "Francesca Toni"
doi: "10.24963/ijcai.2025/481"
url: "https://ijcai.org/proceedings/2025/481"
pdf: "https://ijcai.org/proceedings/2025/0481.pdf"
venue: "Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"
year: 2025
pages: "4321-4329"
track: "Main Track"
keywords:
  - "Knowledge Representation and Reasoning: KRR: Argumentation"
  - "AI Ethics, Trust, Fairness: ETF: Explainability and interpretability"
---

# Counterfactual Explanations Under Model Multiplicity and Their Use in Computational Argumentation

Gianvincenzo Alfano, Adam Gould, Francesco Leofante, Antonio Rago, Francesca Toni

*Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence*, pages 4321-4329. DOI: [10.24963/ijcai.2025/481](https://doi.org/10.24963/ijcai.2025/481)

## Abstract

Counterfactual explanations (CXs) are widely recognised as an essential technique for providing recourse recommendations for AI models. 
However, it is not obvious how to determine CXs in model multiplicity scenarios, where equally performing but different models can be obtained for the same task. 
In this paper, we propose novel qualitative and quantitative definitions of CXs based on explicit, nested quantification over (groups) of model decisions. 
We also study properties of these notions and identify decision problems of interest therefor. 
While our CXs are broadly applicable, in this paper we instantiate them within computational argumentation where model multiplicity naturally emerges, e.g. with incomplete and case-based argumentation frameworks. 
We then illustrate the suitability of our CXs for model multiplicity in legal and healthcare contexts, before analysing the complexity of the associated decision problems.