Scatterplots are widely used in Explainable Artificial Intelligence (XAI) to investigate misclassifications and patterns across instances. However, a significant limitation of scatterplots is overplotting, especially when working with large datasets. Although several quality metrics have been proposed to measure the degree of overplotting, none have been demonstrated to be effective in the context of XAI. This paper aims to evaluate the effectiveness of a quality metric, called OM4AnI, in XAI scenarios. We begin by summarizing two visual patterns—cluster-based and regression-based patterns—that support three common XAI tasks: feature importance, feature dependency, and model accuracy. We also introduce how to select the parameters of OM4AnI based on these patterns. We construct two case studies to identify the effectiveness of OM4AnI using public datasets: Census Income dataset and MNIST dataset. OM4AnI is applied to both scenarios under various visual conditions (e.g., marker size and rendering order) to assess its effectiveness. The results demonstrate that OM4AnI serves as an effective quality metric for these two common XAI scenarios, paving the way for adapting other quality metrics to be scalable within XAI contexts.

ABSTRACT Evaluating synthetic data produced by generative models remains a critical challenge in sensitive domains such as healthcare and finance. Ensuring that such data is ‘faithful’ to real data is essential for downstream applications and decision‐making, including regulatory compliance. This paper introduces an AI‐powered interactive visual system—AIS yst —designed to assess the fidelity of synthetic tabular datasets. The system supports multilevel comparisons with real datasets, spanning multivariate resemblance analyses based on dimensionality reduction through suitable two‐dimensional projections, bivariate correlation and univariate similarity. AIS yst also integrates an AI assistant by leveraging state‐of‐the‐art large language models to summarize key findings and generate suggestions for improving synthetic data generation models. We validated the capabilities of AIS yst through three case studies, supported by feedback from industrial AI experts who endorsed its broader deployment.

Scatterplots are widely used across various domains to identify anomalies in datasets, particularly in multi-class settings, such as detecting misclassified or mislabeled data. However, scatterplot effectiveness often declines with large datasets due to limited display resolution. This paper introduces a novel Visual Quality Measure (VQM) - OM4AnI (Overlap Measure for Anomaly Identification) - which quantifies the degree of overlap for identifying anomalies, helping users estimate how effectively anomalies can be observed in multi-class scatterplots. OM4AnI begins by computing anomaly index based on each data point's position relative to its class cluster. The scatterplot is then discretized into a matrix representation by binning the display space into cell-level (pixel-level) grids and computing the coverage for each pixel. It takes into account the anomaly index of data points covering these pixels and visual features (marker shapes, marker sizes, and rendering orders). Building on this foundation, we sum all the coverage information in each cell (pixel) of matrix representation to obtain the final quality score with respect to anomaly identification. We conducted an evaluation to analyze the efficiency, effectiveness, sensitivity of OM4AnI in comparison with six representative baseline methods that are based on different computation granularity levels: data level, marker level, and pixel level. The results show that OM4AnI outperforms baseline methods by exhibiting more monotonic trends against the ground truth and greater sensitivity to rendering order, unlike the baseline methods. It confirms that OM4AnI can inform users about how effectively their scatterplots support anomaly identification. Overall, OM4AnI shows strong potential as an evaluation metric and for optimizing scatterplots through automatic adjustment of visual parameters.

Model Predictive Control (MPC) method is widely used in autonomous vehicle control technology. The adjustment of MPC weights is crucial for optimizing its control performance, ensuring precise and reliable operation. Traditionally, these weights are adjusted manually, which is inefficient. This study introduces a novel Butterfly Optimization Algorithm (BOA) learning-based method to determine the optimal MPC weights in an efficient way. By adopting the data-driven idea in machine learning, the trajectory data of field experiment human drivers is used to train the controller weights. A simulation-based training platform that enables the automatic training of the MPC controller with varying weights is also developed. Simulation results demonstrate the superior control accuracy and stability performance of BOA learning-based method compared to Linear Quadratic Regulator (LQR) and pure pursuit strategies. The findings suggest that the control method proposed in this research can significantly improve autonomous vehicle control performance and their reliability, thereby contributing to the advancement of autonomous driving technology.