Cited by 277Explainable Artificial Intelligence (XAI)Software Testing and Debugging TechniquesPrivacy-Preserving Technologies in Data

The explanation game: Explaining machine learning models using shapley values

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Merrick, Luke, Taly, Ankur·2020·Lecture notes in computer science

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  1. [2]2. Ancona, M., Ceolini, E., ztireli, C., Gross, M.: Towards better understanding of gradientbased attribution methods for deep neural networks. In: International Conference on Learning Representations (2018)

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Authors
Merrick, L., & Taly, A.
Corresponding author
Merrick, Luke
Venue
Lecture notes in computer science
Published
2020
Volume
LNCS-12279
Pages
17-38
ISSN
  • 0302-9743 (ISSN-L)
  • 1611-3349
OpenAlex
W3038107861
Cited by
277
References
32
Citations by year
2026: 37 · 2025: 68 · 2024: 72 · 2023: 34 · 2022: 31 · 2021: 26 · 2020: 8 · 2019: 1
FWCI
9.27
Type
conference paper
Language
en
Open access
Locations
  • Lecture notes in computer science (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • HAL (submittedVersion, cc-by)
  • arXiv (Cornell University)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
  • Physical Sciences › Computer Science › Software › Software Testing and Debugging Techniques
  • Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
Keywords
Shapley value, explainable machine learning, feature attribution, SHAP, cooperative game theory, confidence intervals, contrastive explanations, cognitive psychology, black-box models, Lending Club

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