Human Interpretable Prototype

Human-interpretable prototypes aim to create machine learning models whose decision-making processes are transparent and understandable to humans, addressing the "black box" problem of many AI systems. Current research focuses on developing "interpretable-by-design" models, often employing prototype-based learning approaches that identify representative data instances explaining model predictions, sometimes leveraging pre-trained foundation models. This work is crucial for building trust in AI, particularly in high-stakes applications like medical diagnosis and financial decision-making, where understanding the reasoning behind AI predictions is paramount for reliable human oversight.