The X Factor: On the Relationship between User eXperience and eXplainability

In this era of complex software systems and artificial intelligence (AI), software in both personal and professional spaces has become more opaque and difficult to use. Research in several disciplines has identified explainability as a means to address these issues, aiming to improve the user experience (UX). While there are notions that explainability interacts with a plethora of UX factors in both positive and negative ways, it is unclear how explanations ultimately affect the overall user experience. To provide an overview of existing research, we examined an SLR on explainability and its influence on various software quality aspects for links to UX factors. Furthermore, we conducted a workshop with 12 UX experts to validate the findings from the literature and identify possible research gaps. The results of our work suggest that explainability has the potential to improve user experience, but that implementing explanations incorrectly poses a substantial risk. We also identify research gaps concerning explainability and user experience and suggest directions for future research.

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Publication Details

Journal
Nordic Conference on Human-Computer Interaction
Published
2024-09-30
DOI
https://doi.org/10.1145/3679318.3685352
Citations
13
Primary Topic
Explainable Artificial Intelligence (XAI)
Type
article
Field-Weighted Citation Impact
1.95
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article

The X Factor: On the Relationship between User eXperience and eXplainability

Timo Speith, Andreas Vogelsang, Kurt Schneider, Verena Klös et al.
13 citations
Nordic Conference on Human-Computer Interaction
Explainable Artificial Intelligence (XAI)
1.95
article

The X Factor: On the Relationship between User eXperience and eXplainability

Timo Speith, Andreas Vogelsang, Kurt Schneider, Verena Klös, Anne Heß, Hannah Deters, Jakob Droste
article en
13 citations

Abstract

In this era of complex software systems and artificial intelligence (AI), software in both personal and professional spaces has become more opaque and difficult to use. Research in several disciplines has identified explainability as a means to address these issues, aiming to improve the user experience (UX). While there are notions that explainability interacts with a plethora of UX factors in both positive and negative ways, it is unclear how explanations ultimately affect the overall user experience. To provide an overview of existing research, we examined an SLR on explainability and its influence on various software quality aspects for links to UX factors. Furthermore, we conducted a workshop with 12 UX experts to validate the findings from the literature and identify possible research gaps. The results of our work suggest that explainability has the potential to improve user experience, but that implementing explanations incorrectly poses a substantial risk. We also identify research gaps concerning explainability and user experience and suggest directions for future research.

Nordic Conference on Human-Computer Interaction
Leibniz University Hannover (DE), Carl von Ossietzky Universität Oldenburg (DE), University of Cologne (DE), Technical University of Applied Sciences Würzburg-Schweinfurt (DE), University of Bayreuth (DE)
Reduced inequalities
Openalex Percentile: Top 12%
Explainable Artificial Intelligence (XAI)
1.95
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