Py-CIU: A Python Library for Explaining Machine Learning Predictions Using Contextual Importance and Utility

Sule Anjomshoae, Timotheus Kampik, Kary Främling

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

Abstract

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
Original languageEnglish
Title of host publicationIJCAI-PRICAI 2020 Workshop on Explainable Artificial Intelligence (XAI)
Publication statusPublished - 2020
MoE publication typeB3 Non-refereed article in conference proceedings
EventWorkshop on Explainable Artificial Intelligence - Virtual, Online, Yokohama, Japan
Duration: 8 Jan 20218 Jan 2021

Workshop

WorkshopWorkshop on Explainable Artificial Intelligence
Country/TerritoryJapan
CityYokohama
Period08/01/202108/01/2021

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