Personalizing the explanation extraction in Intelligent Decision Support Systems

The use of black-box models compromises the adoption of Decision Support Systems because they tipically do not allow the decision maker to understand how decisions are issued by the system. Even with the growth of the Explanable Artificial Intelligence area, little has been done to personalize the e...

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Bibliographic Details
Published in:	2021 16th Iberian Conference on Information Systems and Technologies (CISTI) pp. 1 - 6
Main Authors:	da S. Oliveira, Flavio R., Lima Neto, Fernando B.
Format:	Conference Proceeding
Language:	English
Published:	AISTI 23-06-2021
Subjects:	Data mining Decision support systems explainable artificial intelligence explanation personalization Information systems
Online Access:	Get full text
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Summary:	The use of black-box models compromises the adoption of Decision Support Systems because they tipically do not allow the decision maker to understand how decisions are issued by the system. Even with the growth of the Explanable Artificial Intelligence area, little has been done to personalize the explanations generated in the context of these systems. This article presents an approach to customize explanations of decisions using a graph to mediate inferences. Proofs of concept and analysis of simulated decisions are presented. The results suggest that the approach provided the systems with the ability to issue satisfactory explanations, customizing them for simulated user profiles as long as reasonable levels of accuracy were maintained.
DOI:	10.23919/CISTI52073.2021.9476588