Why this and not that? A Logic-based Framework for Contrastive Explanations
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Abstract (arXiv)
We define several canonical problems related to contrastive explanations, each answering a question of the form ''Why P but not Q?''. The problems compute causes for both P and Q, explicitly comparing their differences. We investigate the basic properties of our definitions in the setting of propositional logic. We show, inter alia, that our framework captures a cardinality-minimal version of existing contrastive explanations in the literature. Furthermore, we provide an extensive analysis of the computational complexities of the problems. We also implement the problems for CNF-formulas using answer set programming and present several examples demonstrating how they work in practice.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 8 of its 18 references that are in the map. First posted on arXiv 11 Jul 2025.
Builds on
8 of 18 references.
References · 8
- 2017Explanation in Artificial Intelligence: Insights from the Social Sciencescited by 1,206
- 2018Abduction-Based Explanations for Machine Learning Modelscited by 160
- 2020On The Reasons Behind Decisionscited by 100
- 2020Model Interpretability through the Lens of Computational Complexitycited by 75
- 2022Logic-Based Explainability in Machine Learningcited by 48
- 2023Logic for Explainable AIcited by 23
- 2024Local vs. Global Interpretability: A Computational Complexity Perspectivecited by 23
- 2022Explainability via Short Formulas: the Case of Propositional Logic with Implementationcited by 4
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Citing papers per year: 2025: 1; 2026: 2.
3 papers.