6–9 Jul 2026
Europe/Warsaw timezone

Visual Explanations of XAI Explainers, to Gain Insight into Predictions from High-Dimensional Models

9 Jul 2026, 10:50
20m
Talks (15-20 minutes) Talks

Speaker

Janith Wanniarachchi (Monash University)

Description

Understanding the behaviour of complex machine learning models has become a challenge in the modern day. Explainable AI (XAI) methods were introduced to provide insights into model predictions, however explaining these explanations can be difficult without proper visualisation methods. In addition, settling the disagreements between these explainers can be difficult based purely on numerical values without a proper visual indicator of the question each method is answering. To fill this gap we have built rosella, an R package offering an interactive Shiny app that visualises model behaviour in the data space alongside XAI explanations. Designed for developers, educators, and students, and based on layered approach towards XAI visualisation, rosella makes model decisions more accessible and interpretable.

Additional Material or Paper

https://github.com/janithwanni/rosella

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Keywords: Please list up to 5 keywords to help us find the right session for your contribution. high dimensional data, explainable ai, interactive tools, machine learning
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Interested in serving as reviewer? janithcwanni@gmail.com

Author

Janith Wanniarachchi (Monash University)

Co-authors

Prof. Dianne Cook (Monash University, Australia) Prof. Kate Saunders (Monash University, Australia) Prof. Patricia Menendez (University of Melbourne, Australia) Dr Thiyanga Talagala (University of Sri Jayewardenepura, Sri Lanka)

Presentation materials

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