The field of vision-language models and tabular data classification is experiencing significant growth, with a focus on improving model performance, robustness, and fairness. Researchers are exploring innovative methods to enhance surrogate models, fine-grained bias exploration, and mitigation techniques. The development of new frameworks, such as the Human-Data-Model Interaction Canvas, is providing fresh perspectives on visual analytics. Furthermore, studies are investigating the capabilities of large language models in reasoning over tabular data and their limitations. Noteworthy papers include Harnessing LLMs Explanations to Boost Surrogate Models in Tabular Data Classification, which proposes a novel in-context learning framework, and Fine-Grained Bias Exploration and Mitigation for Group-Robust Classification, which introduces a method for capturing distributions as a mixture of latent groups. Additionally, the paper Towards Fair In-Context Learning with Tabular Foundation Models explores the fairness implications of tabular in-context learning and proposes preprocessing strategies to address bias.