Interactive visual exploration of latent spaces for explainable ai: bridging concepts and features

Latent space exploration offers a powerful lens for interpreting and improving the explainability of black-box AI models. This paper introduces a visual interface based on (beta)-Variational Autoencoders that enables users to navigate latent spaces interactively. By employing tools like visual latent sliders and transformation pathways, the interface demonstrates how latent dimensions can influence image representations, uncovering semantic structure and disentangled features. Using the MedMNIST medical image dataset, we illustrate the potential of this approach to bridge the gap between technical latent space analysis and intuitive understanding. Although the focus is on presenting the methodology, this work sets the stage for integrating user interaction and metrics, particularly in high-stakes domains such as medical imaging.