Explainable Classification

Explainable classification aims to build machine learning models that not only achieve high accuracy in assigning data points to categories but also provide transparent and understandable explanations for their predictions. Current research focuses on developing inherently interpretable models, such as those based on prototypes, rule learning, and specific neural architectures like neural cellular automata, alongside methods that generate post-hoc explanations for existing black-box models (e.g., using attention mechanisms or feature importance scores). This field is crucial for building trust in AI systems, particularly in high-stakes domains like medicine and finance, where understanding the reasoning behind a model's decisions is paramount for responsible deployment and effective human-AI collaboration.