Built in Interpretability

Built-in interpretability in machine learning aims to design models that inherently provide understandable explanations for their predictions, unlike traditional "black-box" models requiring post-hoc analysis. Current research focuses on developing inherently interpretable architectures, such as concept bottleneck models and prototype-based networks, and employing techniques like constrained optimization and probabilistic modeling to enhance transparency. This pursuit is crucial for building trust in AI systems, particularly in high-stakes applications like healthcare and finance, and for advancing scientific understanding of complex models by providing insights into their internal decision-making processes.