A Quality Metric to Improve Scatterplots for Explainable AI

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dc.contributor.authorLiu, Liqunen_US
dc.contributor.authorRuddle, Roy A.en_US
dc.contributor.authorBogachev, Leonid V.en_US
dc.contributor.authorRezaei, Mahdien_US
dc.contributor.authorKhara, Arjunen_US
dc.contributor.editorKucher, Kostiantynen_US
dc.contributor.editorDiehl, Alexandraen_US
dc.contributor.editorGillmann, Christinaen_US
dc.date.accessioned2024-05-21T08:44:55Z
dc.date.available2024-05-21T08:44:55Z
dc.date.issued2024
dc.description.abstractScatterplots are widely utilised in Explainable Artificial Intelligence (XAI) to investigate misclassifications and patterns among instances. However, when datasets are large, overplotting diminishes the effectiveness of scatterplots. This poster introduces a new quality metric to measure the overplotting of scatterplots in the context of XAI. Initially, we assess the significance of each data point within a scatterplot by continuous density transformation, Mahalanobis Distance and a mapping function. Building on this foundation, we develop a quality metric for scatterplots. Our metric performs well accounting for rendering orders and marker sizes in scatterplots, showcasing the metric's potential to improve the effectiveness of XAI scatterplots.en_US
dc.description.sectionheadersPosters
dc.description.seriesinformationEuroVis 2024 - Posters
dc.identifier.doi10.2312/evp.20241077
dc.identifier.isbn978-3-03868-258-5
dc.identifier.pages3 pages
dc.identifier.urihttps://doi.org/10.2312/evp.20241077
dc.identifier.urihttps://diglib.eg.org/handle/10.2312/evp20241077
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCCS Concepts: Human-centered computing → Visualisation design and evaluation methods; Computing methodologies → Machine learning
dc.subjectHuman centered computing → Visualisation design and evaluation methods
dc.subjectComputing methodologies → Machine learning
dc.titleA Quality Metric to Improve Scatterplots for Explainable AIen_US
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