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 language | English |
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Title of host publication | 27th International Conference on Intelligent User Interfaces, IUI 2022 Companion |
Publisher | Association for Computing Machinery |
Pages | 120-123 |
Number of pages | 4 |
ISBN (Electronic) | 9781450391450 |
DOIs | |
Publication status | Published - 22 Mar 2022 |
Externally published | Yes |
Event | 27th International Conference on Intelligent User Interfaces, IUI 2022 - Virtual, Online, Finland Duration: 22 Mar 2022 → 25 Mar 2022 |
Publication series
Name | International Conference on Intelligent User Interfaces, Proceedings IUI |
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Conference
Conference | 27th International Conference on Intelligent User Interfaces, IUI 2022 |
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Country/Territory | Finland |
City | Virtual, Online |
Period | 22/03/22 → 25/03/22 |
Bibliographical note
Publisher Copyright:© 2022 Owner/Author.
Funding
This work was supported by the Research Foundation–Flanders (FWO, grant G0A3319N) and the imec.icon project AIDA financed by Flanders Innovation & Entrepreneurship (grant HB.2020.2373). This work was supported by the Research Foundation-Flanders (FWO, grant G0A3319N) and the imec.icon project AIDA financed by Flanders Innovation and Entrepreneurship (grant HB.2020.2373)
Funders | Funder number |
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Research Foundation Flanders | |
Research Foundation Flanders | |
Agentschap Innoveren en Ondernemen | HB.2020.2373 |
Fonds Wetenschappelijk Onderzoek | G0A3319N |
Keywords
- algorithmic transparency
- explainability
- information visualisation
- interpretability
- XAI