An Evaluation of Contextual Importance and Utility for Outcome Explanation of Black-Box Predictions for Medical Datasets

Avleen Malhi*, Kary Främling

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review


Contextual Importance and Utility (CIU) is a model-agnostic method for producing situation- or instance-specific explanations of the outcome of so-called black-box systems. A major difference between CIU and other outcome explanation methods (also called post-hoc methods) is that CIU produces explanations without producing any intermediate interpretable model. CIU’s notion of importance is similar as in Decision Theory but differs from how importance is defined for other outcome explanation methods. Utility is also a well-known concept from Decision Theory that is largely ignored in current Explainable AI research. CIU is here validated by providing explanations for the two popular medical data sets - heart disease and breast cancer in order to show the applicability of CIU explanations on medical predictions and with different black-box models. The explanations are compared with corresponding ones produced by the Local Interpretable Model-agnostic Explanations (LIME) method [17], which is currently one of the most used post-hoc explanation methods. The paper’s main contribution is to provide new CIU results and insights on several benchmark data sets and showing in what way CIU differs from LIME-based explanations.

Original languageEnglish
Title of host publicationExplainable Artificial Intelligence - 1st World Conference, xAI 2023, 2023, Proceedings
EditorsLuca Longo
Number of pages14
ISBN (Print)978-3-031-44063-2
Publication statusPublished - 2023
MoE publication typeA4 Conference publication
EventWorld Conference on eXplainable Artificial Intelligence - Lisbon, Portugal
Duration: 26 Jul 202328 Jul 2023
Conference number: 1

Publication series

NameCommunications in Computer and Information Science
Volume1901 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937


ConferenceWorld Conference on eXplainable Artificial Intelligence
Abbreviated titlexAI


  • Breast cancer data
  • Contextual Importance
  • Contextual Utility
  • Explainable AI
  • Heart disease
  • Multiple Criteria Decision Making


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