Interpretable by Design

Interpretable-by-design (IbD) focuses on creating machine learning models and interfaces that are inherently transparent and understandable, addressing the "black box" problem of many complex AI systems. Current research emphasizes developing novel model architectures, such as those based on prototypes, additive models, and mixtures of experts, alongside user-centered interface designs that effectively communicate model behavior to non-experts. This approach is crucial for building trust in AI, particularly in high-stakes domains like healthcare and autonomous systems, and facilitates better model understanding, debugging, and responsible deployment.