ILIME: Local and Global Interpretable Model-Agnostic Explainer of Black-Box Decision

Radwa ElShawi, Youssef Sherif, Mouaz Al-Mallah, Sherif Sakr

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Scopus citations


Despite outperforming humans in different supervised learning tasks, complex machine learning models are criticised for their opacity which make them hard to trust especially when used in critical domains (e.g., healthcare, self-driving car). Understanding the reasons behind the decision of a machine learning model provides insights into the model and transforms the model from a non-interpretable model (black-box) to an interpretable one that can be understood by humans. In addition, such insights are important for identifying any bias or unfairness in the decision made by the model and ensure that the model works as expected. In this paper, we present ILIME, a novel technique that explains the prediction of any supervised learning-based prediction model by relying on an interpretation mechanism that is based on the most influencing instances for the prediction of the instance to be explained. We demonstrate the effectiveness of our approach by explaining different models on different datasets. Our experiments show that ILIME outperforms a state-of-the-art baseline technique, LIME, in terms of the quality of the explanation and the accuracy in mimicking the behaviour of the black-box model. In addition, we present a global attribution technique that aggregates the local explanations generated from ILIME into few global explanations that can mimic the behaviour of the black-box model globally in a simple way.

Original languageEnglish (US)
Title of host publicationAdvances in Databases and Information Systems - 23rd European Conference, ADBIS 2019, Proceedings
EditorsTatjana Welzer, Vili Podgorelec, Aida Kamišalic Latific, Johann Eder
Number of pages16
ISBN (Print)9783030287290
StatePublished - 2019
Event23rd European Conference on Advances in Databases and Information Systems, ADBIS 2019 - Bled, Slovenia
Duration: Sep 8 2019Sep 11 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11695 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other23rd European Conference on Advances in Databases and Information Systems, ADBIS 2019


  • Interpretability
  • Machine learning
  • Model-agnostic

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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