Abduction-Based Explanations for Machine Learning Models
Open accessAbstract
The growing range of applications of Machine Learning (ML) in a multitude of settings motivates the ability of computing small explanations for predictions made. Small explanations are generally accepted as easier for human decision makers to understand. Most earlier work on computing explanations is based on heuristic approaches, providing no guarantees of quality, in terms of how close such solutions are from cardinality- or subset-minimal explanations. This paper develops a constraint-agnostic solution for computing explanations for any ML model. The proposed solution exploits abductive reasoning, and imposes the requirement that the ML model can be represented as sets of constraints using some target constraint reasoning system for which the decision problem can be answered with some oracle. The experimental results, obtained on well-known datasets, validate the scalability of the proposed approach as well as the quality of the computed solutions.
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Examples of compactness metrics include the number of features in an explanation, average path length in a decision tree, reduction with respect to the complete data sample, and the number of decision rules in a set.
“Examples include: the number of features in an explanation (e.g., [54,109,192,204,223,265]), average path length in a decision tree (e.g., [135,276,277]), reduction w.r.t.complete data sample (e.g., [7,33,109]), or the number of decision rules in a set (e.g., [137,144,203,223,266,302]).”
From From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI (2023) · cites this paper as [109]
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Self-citation
- Authors
- Ignatiev, A., Narodytska, N., & Marques-Silva, J.
- Affiliations
- University of Lisbon (PT)
- VMware (United States) (US)
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2019-07-17
- Volume
- 33
- Issue
- 01
- Pages
- 1511-1519
- ISSN
- 2159-5399 (ISSN-L)
- 2374-3468
- OpenAlex
- W2902934778
- Cited by
- 40
- References
- 43
- Citations by year
- 2025: 1 · 2024: 3 · 2023: 14 · 2022: 13 · 2021: 7 · 2020: 2
- FWCI
- 2.10
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- www.aaai.org/ (unspecified)
- Locations
- Proceedings of the AAAI Conference on Artificial Intelligence (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Fundação para a Ciência e a Tecnologia (01-0145, PTDC/CCI-COM/29300/2017, /2017, SFRH/BPD/120315/2016, CEECIND/04549/2017/CP1387/CT0037, LISBOA-01-0145-FEDER)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Physical Sciences › Computer Science › Artificial Intelligence › Logic, Reasoning, and Knowledge
- Physical Sciences › Computer Science › Information Systems › Data Mining Algorithms and Applications
- Keywords
- abductive reasoning, constraint-based reasoning, minimal explanations, explainable machine learning
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