Embeddings are not directly interpretable in the way human-readable features or rules are. An embedding is a dense vector (a list of numbers) that represents data—like words, images, or user preferences—in a lower-dimensional space. While these vectors capture patterns and relationships in the data, the individual dimensions of the vector rarely map to specific, understandable concepts. For example, in a word embedding, a dimension might loosely correlate with “plurality” or “gender,” but this isn’t guaranteed or explicitly defined. The lack of clear semantic labels for each dimension makes it difficult to explain why a particular embedding value leads to a specific model prediction or behavior.
However, embeddings can be indirectly analyzed to uncover insights. Techniques like dimensionality reduction (e.g., PCA, t-SNE) or clustering can visualize embeddings in 2D/3D space, revealing patterns like groupings of similar words or images. For instance, in a word embedding model like Word2Vec, plotting embeddings might show that “dog,” “cat,” and “horse” cluster together, while “car,” “plane,” and “train” form another group. Similarly, in recommendation systems, user/item embeddings might cluster users with similar tastes. These methods don’t explain individual vector values but highlight broader relationships. Developers can also probe embeddings by testing analogies (e.g., “king - man + woman ≈ queen”) to validate semantic relationships, though this is more about verifying expected behavior than true interpretability.
The practical takeaway is that embeddings are powerful for capturing complex data relationships but aren’t designed for transparency. If interpretability is critical—like in healthcare or finance—developers might combine embeddings with techniques like attention mechanisms (which highlight influential input parts) or use simpler models alongside embeddings for post-hoc analysis. For example, a movie recommender could use embeddings to represent users and films but pair them with a logistic regression layer whose coefficients indicate which movie genres drive recommendations. In short, embeddings trade interpretability for efficiency and performance, requiring complementary tools to bridge the gap between raw vectors and human understanding.