Tabular embedding methods have become increasingly popular due to their effectiveness in improving the results of various tasks, including classic databases tasks and machine learning predictions. However, most current methods treat these embedding models as "black boxes" making it difficult to understand the insights captured by the models. Our research proposes a novel approach to interpret these models, aiming to provide local and global explanations for the original data and detect potential flaws in the embedding models. The proposed solution is appropriate for every tabular embedding algorithm, as it fits the black box view of the embedding model. Furthermore, we propose methods for comparing different embedding models, which can help identify data biases that might impact the models' credibility without the user's knowledge. Our approach is evaluated on multiple datasets and multiple embeddings, demonstrating that our proposed explanations provide valuable insights into the behavior of tabular embedding methods. By making these models more transparent, we believe our research will contribute to the development of more effective and reliable embedding methods for a wide range of applications.

We present TabEE, Tab ular E mbedding E xplanations, a framework designed to generate explanations for interpreting tabular embedding models. Our framework aims to furnish both local and global explanations for the original data, facilitating the detection of potential flaws in embedding models. TabEE is versatile and compatible with any tabular embedding algorithm, as it adheres to the black box perspective of embedding models. The generated explanations also enable comparisons between multiple embedding models. This demonstration illustrates the effectiveness of TabEE in providing interpretable insights into tabular embedding models, contributing to improved model understanding and credibility assessment.