mlsanityData to 14 Oct 2026

Why this and not that? A Logic-based Framework for Contrastive Explanations

arXiv 2507.08454 ↗First posted 11 Jul 2025cs.AI cs.LG cs.LOcited by 3

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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

  1. 2017Explanation in Artificial Intelligence: Insights from the Social Sciencescited by 1,206
  2. 2018Abduction-Based Explanations for Machine Learning Modelscited by 160
  3. 2020On The Reasons Behind Decisionscited by 100
  4. 2020Model Interpretability through the Lens of Computational Complexitycited by 75
  5. 2022Logic-Based Explainability in Machine Learningcited by 48
  6. 2023Logic for Explainable AIcited by 23
  7. 2024Local vs. Global Interpretability: A Computational Complexity Perspectivecited by 23
  8. 2022Explainability via Short Formulas: the Case of Propositional Logic with Implementationcited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.