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Py-CIU: A Python Library for Explaining Machine Learning Predictions Using Contextual Importance and Utility
Umeå University, Faculty of Science and Technology, Department of Computing Science. (XAI)ORCID iD: 0000-0002-1232-346X
Umeå University, Faculty of Science and Technology, Department of Computing Science.ORCID iD: 0000-0002-6458-2252
Umeå University, Faculty of Science and Technology, Department of Computing Science.ORCID iD: 0000-0002-8078-5172
2020 (English)In: Proceedings, 2020Conference paper, Oral presentation only (Other academic)
Abstract [en]

In this paper, we present the Py-CIU library, a generic Python tool for applying the Contextual Importance and Utility (CIU) explainable machine learning method. CIU uses concepts from decision theory to explain a machine learning model’s prediction specific to a given data point by investigating the importance and usefulness of individual features (or feature combinations) to a prediction. The explanations aim to be intelligible to machine learning experts as well as non-technical users. The library can be applied to any black-box model that outputs a prediction value for all classes

Place, publisher, year, edition, pages
2020.
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:umu:diva-174782OAI: oai:DiVA.org:umu-174782DiVA, id: diva2:1464596
Conference
IJCAI-PRICAI 2020 Workshop on Explainable Artificial Intelligence (XAI), january 8, 2020
Note

Conference postponed from July 2020 to preliminary January 2021. 

Available from: 2020-09-07 Created: 2020-09-07 Last updated: 2021-02-11Bibliographically approved

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fulltext(1032 kB)503 downloads
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Anjomshoae, SuleKampik, TimotheusFrämling, Kary

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf