mlsanityData to 14 Oct 2026

On Interactive Explanations as Non-Monotonic Reasoning

arXiv 2208.00316 ↗First posted 30 Jul 2022cs.AIcited by 3

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Abstract (arXiv)

Recent work shows issues of consistency with explanations, with methods generating local explanations that seem reasonable instance-wise, but that are inconsistent across instances. This suggests not only that instance-wise explanations can be unreliable, but mainly that, when interacting with a system via multiple inputs, a user may actually lose confidence in the system. To better analyse this issue, in this work we treat explanations as objects that can be subject to reasoning and present a formal model of the interactive scenario between user and system, via sequences of inputs, outputs, and explanations. We argue that explanations can be thought of as committing to some model behaviour (even if only prima facie), suggesting a form of entailment, which, we argue, should be thought of as non-monotonic. This allows: 1) to solve some considered inconsistencies in explanation, such as via a specificity relation; 2) to consider properties from the non-monotonic reasoning literature and discuss their desirability, gaining more insight on the interactive explanation scenario.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 4 of its 18 references that are in the map. First posted on arXiv 30 Jul 2022.

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4 of 18 references.

References · 4

  1. 2019Make Up Your Mind! Adversarial Generation of Inconsistent Natural Language Explanationscited by 71
  2. 2021GLocalX -- From Local to Global Explanations of Black Box AI Modelscited by 48
  3. 2022ExSum: From Local Explanations to Model Understandingcited by 15
  4. 2021Monotonicity and Noise-Tolerance in Case-Based Reasoning with Abstract Argumentation (with Appendix)cited by 6

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Citing papers per year: 2023: 1; 2024: 0; 2025: 2.

3 papers.