Machine learning models trained on vast amounts of data have achieved remarkable success across various applications. However, they also pose new challenges and risks for deployment in real-world high-stakes domains. Decisions made by deep learning models are often difficult to interpret, and the underlying mechanisms remain poorly understood. Given that deep learning models operate as black-boxes, it is challenging to understand, much less resolve, various types of failures in current machine learning systems.

In this talk, I will describe our work towards building transparent machine learning systems through the lens of representation learning. First, I will present a white-box approach to understanding transformer models. I will show how to derive a family of mathematically interpretable transformer-like deep network architectures by maximizing the information gain of the learned representations. Furthermore, I will demonstrate that the proposed interpretable transformer achieves competitive empirical performance on large-scale real-world datasets, while learning more interpretable and structured representations than black-box transformers. Next, I will present our work on training the first set of vision and vision-language foundation models with rigorous differential privacy guarantees, and demonstrate the promise of high-utility differentially private representation learning. To conclude, I will discuss future directions towards transparent and safe AI systems we can understand and trust.