Explainable Decision

Explainable decision-making in artificial intelligence focuses on developing models and methods that not only produce accurate predictions but also provide understandable justifications for their choices. Current research emphasizes integrating interpretability directly into model architectures, such as through decision trees, graph transformers, and rule-based systems augmented by large language models, rather than relying solely on post-hoc explanations. This pursuit is crucial for building trust in AI systems across diverse applications, from autonomous vehicles and medical diagnosis to financial forecasting and customs classification, ensuring both reliability and accountability.