A contrastive account of explanation generation
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In this article, we propose a contrastive account of explanation generation. Though researchers have long wrestled with the concepts of explanation and understanding, as well as with the procedures by which we might evaluate explanations, less attention has been paid to the initial generation stages of explanation. Before an explainer can answer a question, he or she must come to some understanding of the explanandum-what the question is asking-and of the explanatory form and content called for by the context. Here candidate explanations are constructed to respond to the particular interpretation of the question, which, according to the pragmatic approach to explanation, is constrained by a contrast class-a set of related but nonoccurring alternatives to the topic that emerge from the surrounding context and the explainer's prior knowledge. In this article, we suggest that generating an explanation involves two operations: one that homes in on an interpretation of the question, and a second one that locates an answer. We review empirical work that supports this account, consider the implications of these contrastive processes, and identify areas for future study.
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Martens and Provost suggest the model-agnostic Search for Explanations for Document Classification algorithm for retrieving counterfactual explanations.
“Hence, they suggest the model-agnostic ''Search for Explanations for Document Classification'' algorithm for retrieving counterfactual explanations.”
From A Survey of Contrastive and Counterfactual Explanation Generation Methods for Explainable Artificial Intelligence (2021) · cites this paper as [146]
The Search for Explanations for Document Classification algorithm is only directly applicable to binary linear classifiers, whereas heuristics are proposed for nonlinear models.
“However, it is only directly applicable to binary linear classifiers, whereas heuristics are proposed for nonlinear models.”
From A Survey of Contrastive and Counterfactual Explanation Generation Methods for Explainable Artificial Intelligence (2021) · cites this paper as [146]
Martens and Provost argue that decision trees are an inadequate tool for representing large documents.
“In contrast, Martens and Provost argue that decision trees are an inadequate tool for representing, e.g., large documents [146].”
From A Survey of Contrastive and Counterfactual Explanation Generation Methods for Explainable Artificial Intelligence (2021) · cites this paper as [146]
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- Authors
- Chin-Parker, S., & Bradner, A.
- Affiliations
- Kenyon College (US)
- Venue
- Psychonomic Bulletin & Review
- Published
- 2017-07-31
- Volume
- 24
- Issue
- 5
- Pages
- 1387-1397
- ISSN
- 1069-9384 (ISSN-L)
- 1531-5320
- OpenAlex
- W2742043411
- Cited by
- 15
- References
- 75
- Citations by year
- 2026: 2 · 2025: 2 · 2024: 1 · 2023: 2 · 2021: 4 · 2020: 2 · 2019: 1 · 2017: 1
- FWCI
- 6.37
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Psychonomic Bulletin & Review (publishedVersion)
- PubMed (publishedVersion)
- Digital Kenyon (Kenyon College) (submittedVersion)
- Topics
- Social Sciences › Arts and Humanities › Language and Linguistics › Syntax, Semantics, Linguistic Variation
- Social Sciences › Psychology › Developmental and Educational Psychology › Child and Animal Learning Development
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Categorization, perception, and language
- Keywords
- explanation generation, contrastive reasoning, question comprehension, understanding, explanation evaluation, prior knowledge
- MeSH terms
- Humans, Thinking, Comprehension
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