Explaining Artificial Intelligence with Tailored Interactive Visualisations

Jeroen Ooge, Katrien Verbert*

*Corresponding author for this work

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

Abstract

Artificial intelligence (AI) is becoming ubiquitous in the lives of both researchers and non-researchers, but AI models often lack transparency. To make well-informed and trustworthy decisions based on these models, people require explanations that indicate how to interpret the model outcomes. This paper presents our ongoing research in explainable AI, which investigates how visual analytics interfaces and visual explanations, tailored to the target audience and application domain, can make AI models more transparent and allow interactive steering based on domain expertise. First, we present our research questions and methods, contextualised by related work at the intersection of AI, human-computer interaction, and information visualisation. Then, we discuss our work so far in healthcare, agriculture, and education. Finally, we share our research ideas for additional studies in these domains.

Original languageEnglish
Title of host publication27th International Conference on Intelligent User Interfaces, IUI 2022 Companion
PublisherAssociation for Computing Machinery (ACM)
Pages120-123
Number of pages4
ISBN (Electronic)9781450391450
DOIs
Publication statusPublished - 22 Mar 2022
Externally publishedYes
Event27th International Conference on Intelligent User Interfaces, IUI 2022 - Virtual, Online, Finland
Duration: 22 Mar 202225 Mar 2022

Publication series

NameInternational Conference on Intelligent User Interfaces, Proceedings IUI

Conference

Conference27th International Conference on Intelligent User Interfaces, IUI 2022
Country/TerritoryFinland
CityVirtual, Online
Period22/03/2225/03/22

Keywords

  • algorithmic transparency
  • explainability
  • information visualisation
  • interpretability
  • XAI

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