Liu, LiqunRuddle, Roy A.Bogachev, Leonid V.Rezaei, MahdiKhara, ArjunKucher, KostiantynDiehl, AlexandraGillmann, Christina2024-05-212024-05-212024978-3-03868-258-5https://doi.org/10.2312/evp.20241077https://diglib.eg.org/handle/10.2312/evp20241077Scatterplots 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.Attribution 4.0 International LicenseCCS Concepts: Human-centered computing → Visualisation design and evaluation methods; Computing methodologies → Machine learningHuman centered computing → Visualisation design and evaluation methodsComputing methodologies → Machine learning10.2312/evp.202410773 pages