Interpretable Embeddings

Interpretable embeddings aim to create machine learning representations that are both highly informative and easily understood by humans, addressing the "black box" nature of many powerful models. Current research focuses on generating interpretable embeddings through various methods, including prompting large language models to generate descriptive questions, utilizing autoencoders with constraints for specific data types, and designing novel architectures like prototypical networks for inherent interpretability during model training. This work is crucial for enhancing trust and understanding in complex AI systems, particularly in sensitive applications like neuroscience and climate modeling, where transparency and explainability are paramount.