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Explaining Multi-Criteria Decision Aiding Models with an Extended Shapley Value
Open accessAbstract
The capability to explain the result of aggregation models to decision makers is key to reinforcing user trust. In practice, Multi-Criteria Decision Aiding models are often organized in a hierarchical way, based on a tree of criteria. We present an explanation approach usable with any hierarchical multi-criteria model, based on an influence index of each attribute on the decision. A set of desirable axioms are defined. We show that there is a unique index fulfilling these axioms. This new index is an extension of the Shapley value on trees. An efficient rewriting of this index, drastically reducing the computation time, is obtained. Finally, the use of the new index is illustrated on an example.
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The Single Deletion method allows checking for specific properties, such as the null attribute indicating that omitting a feature that has no effect on the output of the model should have an importance score of zero.
“The Single Deletion method also allows to check for specific properties, such as the “null attribute” indicating that omitting a feature that has no effect on the output of the model, should have an importance score of zero [133].”
From From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI (2023) · cites this paper as [133]
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Metadata
- Authors
- Labreuche, C., & Fossier, S.
- Affiliations
- Thales (France) (FR)
- Venue
- Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
- Published
- 2018-07
- Pages
- 331-339
- OpenAlex
- W2808381567
- Cited by
- 35
- References
- 24
- Citations by year
- 2026: 1 · 2025: 4 · 2024: 1 · 2023: 7 · 2022: 6 · 2021: 4 · 2020: 9 · 2019: 3
- FWCI
- 5.91
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- International Joint Conference on Artificial Intelligence (publishedVersion)
- Funders
- Horizon 2020 Framework Programme
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Multi-Criteria Decision Making
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Game Theory and Voting Systems
- Keywords
- multicriteria decision aiding, Shapley value, hierarchical criteria, influence index, axiomatization, decision tree, user trust, computation time reduction
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