Concept Based

Concept-based explainable AI (XAI) focuses on interpreting complex machine learning models' predictions using high-level, human-understandable concepts rather than low-level features. Current research emphasizes developing model architectures, such as concept bottleneck models and various concept activation vector methods, that explicitly incorporate concept learning and representation, often leveraging vision-language models or knowledge graphs to define and discover these concepts. This field is significant because it addresses the critical need for trustworthy and transparent AI systems, improving both the understanding of model behavior and the reliability of their predictions across diverse applications, including healthcare and autonomous driving.